Clinical Efficacy and Patient Experience of Adjunctive Hyaluronic Acid-based Gel Application With Coronally Advanced Flap in Type 1 Gingival Recession: A Randomized Controlled Clinical Trial

Clinical Efficacy and Patient Experience of Adjunctive Hyaluronic Acid-based Gel Application With Coronally Advanced Flap in Type 1 Gingival Recession: A Randomized Controlled Clinical Trial | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Clinical Efficacy and Patient Experience of Adjunctive Hyaluronic Acid-based Gel Application With Coronally Advanced Flap in Type 1 Gingival Recession: A Randomized Controlled Clinical Trial Fadime ÜLVAN, Figen ÖNGÖZ DEDE This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9114410/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Objective Hyaluronic acid (HA) has been suggested as a local chemotherapeutic agent in periodontal treatments due to its role in wound healing and periodontal regeneration. The aim of this study is to evaluate the effect of HA use in addition to the coronally advanced flap (CAF) technique in type 1 gingival recession (RT1) on postoperative morbidity, clinical parameters, and patient satisfaction, analyzed at baseline and 6 months. Materials and Methods This split-mouth study included 20 patients (9 males, 11 females) with a mean age of 42.10 ± 9.46 years who were diagnosed with bilateral RT1 gingival recession in the maxilla. The right and left defect areas of the 20 patients were randomly assigned to two groups. The control group received CAF (n = 20), and the test group received CAF + HA (n = 20). Pre-treatment plaque index (PI), gingival index (GI), probing pocket depth (PPD), clinical attachment level (CAL), vertical recession depth (VRD), horizontal recession width (HRW), gingival thickness (GT), attached gingival width (AGW), and keratinized gingival width (KGW) were recorded. Additionally, a satisfaction analysis was conducted, including patient-based comfort (PCS), aesthetic (PAS), and sensitivity (PSS) scores. During the operation, a 0.8% HA gel was applied to the defect areas in the test group, in addition to CAF, while saline was applied to the control group, also in addition to CAF. Pain, bleeding, and edema scores, as well as the number of painkillers, were recorded during the 9-day postoperative period. Clinical parameter measurements and patient satisfaction analyses were repeated 6 months after surgical treatment. Results When intra-group comparisons were evaluated, statistically significant improvements were found in clinical parameters, including PPD, CAL, VRD, HRW, GT, AG, and KGW, and patient satisfaction analysis at 6 months compared to baseline in both groups ( P 0.05), the control group showed an increase at 6 months compared to baseline ( P < 0.05). In the intergroup comparison, a significantly greater increase in PBC was observed in the test group compared to the control group at 6 months ( P 0.05). Results Our study suggests that addictive HA to CAF may improve GI and PCS, thereby increase treatment effectiveness and facilitating patient tolerance. Clinical relevance: The application of 0.8% HA gel in combination with CAF provides positive results in GI and PCS in patients with RT1 gingival recession compared to CAF alone. Hyaluronic acid Coronally positioned flap Gingival recession Wound healing Split mouth design Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Gingival recession (GR) causes cervical dentin sensitivity, plaque accumulation, and gingival inflammation, root caries, aesthetic concerns, and psychological problems, such as the patient's fear of losing their teeth [ 1 ]. The literature considers the application of connective tissue grafting (CTG) with a coronally advanced flap (CAF) to be the gold standard among surgical procedures for root coverage [ 2 , 3 ]. However, CTG has disadvantages such as the need for a donor site, increased patient morbidity, postoperative bleeding, and insufficient tissue acquisition [ 2 , 4 ]. Furthermore, Cairo et al. 5 reported that CAF applied alone in cases where gingival thickness (GT) > 0.8 mm yielded results complete root coverage (CRC) and reduction in recession depth similar to those obtained with CAF applied in combination with CTG. Thus, researchers have sought different materials for surgical treatment of GR, such as enamel matrix derivatives [ 6 ], acellular dermal matrix [ 7 ], xenogeneic collagen matrix [ 8 ], platelet-rich plasma [ 9 ], platelet-rich fibrin [ 4 ]; concentrated growth factors [ 10 ], and more recently, hyaluronic acid (HA) [ 11 , 12 ], have emerged as biomaterials and alternative grafts. Hyaluronic acid is a branched, single-chain polysaccharide that is endogenously present in all living organisms and plays a vital role in the extracellular spaces of body tissues [ 13 ]. In wound-healing and tissue-repair processes, HA, an endogenous compound, has been shown to play an active role by maintaining a moist environment conducive to healing and by stimulating the migration of growth factors, cellular components, and various cells necessary for healing [ 14 ]. Furthermore, Casale et al. 15 reported that topically applied hyaluronic acid could be used as an adjunctive treatment to reduce patient discomfort and accelerate healing in the postoperative period following implant and sinus lift procedures, as well as for the treatment of gingivitis and chronic periodontitis. The investigation of HA efficacy in the treatment of GR is relatively new compared to other biomaterials used in this field [ 16 ]. In an animal study investigating the histological outcomes of HA use in the treatment of GR, the authors reported that when CAF was applied in combination with cross-linked hyaluronic acid, the test group showed a statistically significant increase in periodontal ligament (PDL)-like tissue and cementum containing new bone compared to the control group treated with CAF alone [ 12 ]. The researchers also reported that newly formed collagen fibers in the control areas ran parallel to the root surface, but in the test areas, these fibers were inclined toward the root surface [ 12 ]. A recent clinical study found that CAF + HA (0.2% gengigel) for the treatment of GR did not demonstrate superiority over CAF alone or CAF with membrane techniques on clinical parameters at the 6-month follow-up [ 17 ]. Moreover, a recent meta-analysis sought to determine the effect of HA on the treatment of GR [ 16 ]. However, a clear conclusion could not be reached due to the limited number of studies in literature, the heterogeneity of the control groups across the three included studies, and differences in follow-up periods [ 16 ]. The researchers suggested that more controlled randomized trials are needed in this area [ 16 ]. Within the scope of the available information, a literature review conducted to date has found very limited sources evaluating the use of hyaluronic acid in combination with CAF for the treatment of GR. While these sources frequently evaluate the clinical success of HA in root coverage procedures, its effects on patient experience have not been evaluated. We hypothesized that the additional use of hyaluronic acid in root coverage procedures would increase root coverage clinical success rates and patient aesthetic scores, accelerate wound healing, and reduce postoperative complaints such as pain, discomfort, and bleeding. Thus, the aim of this study is to evaluate the effect of hyaluronic acid-based gel applied with the CAF technique on clinical parameters, postoperative morbidity, and patient aesthetic scores in Type 1 (RT1) gingival recessions. Materials and Methods Volunteers for this randomized controlled clinical trial were selected from patients who visited the Periodontology Clinic of Ordu University Faculty of Dentistry between September 2024 and July 2025 due to aesthetic and sensitivity complaints caused by GR. The study protocol was approved by the Ondokuz Mayıs University Clinical Research Ethics Committee, Samsun, Türkiye (Approval Number: 2024/178) and deemed appropriate for initiation by the Türkiye Ministry of Health's Medicines and Medical Devices Agency (Approval Number: 2024-031). Furthermore, the study was conducted in accordance with the 1975 Declaration of Helsinki, as revised in 2013. The trial is registered with ClinicalTrials.gov and is identified as NCT07437885. Study population and design At the start of the study, the GR of participants' assessment was defined according to the criteria for periodontal manifestations of systemic diseases and developmental and acquired conditions outlined in the consensus report of the 3rd working group of the 2017 World Workshop on Classification of Periodontal and Peri-Implant Diseases and Conditions [ 18 ]. The evaluation of GR in volunteers was defined according to the criteria established by Cairo et al. in 2011 19 . According to these criteria, recession without interproximal attachment loss and where the interproximal cement-enamel junction (CEJ) can’t be clinically identified on the distal and mesial sides of the tooth was considered type I recession (RT1). Twenty individuals with bilateral multiple RT1 GR in the maxilla, with an average age of 42.34 ± 9.60, including 9 women and 11 men, were included in our study (Table 1 ). The maxillary right and left incisors, canines, premolars, and first molars were included in our study. The individuals included in the study had 4 upper right central, 4 upper right lateral, 12 upper right canine, 12 upper right first premolar, 6 upper right second premolar, 7 upper right first molars, 4 upper left central incisors, 4 upper left lateral incisors, 13 upper left canines, 13 upper left first premolars, 2 upper left second premolars, and 5 upper left first molars, totaling 86 teeth. Individuals meeting the criteria were selected by a single researcher (FÜ) at the Periodontology Clinic of the Faculty of Dentistry, Ordu University. All volunteers included in the study received oral hygiene training, and phase 1 non-surgical periodontal treatments were completed. Participants with systemic diseases affecting wound healing, those who had undergone periodontal surgical treatment in the relevant areas, those with active periodontal disease, smokers, pregnant and breastfeeding women, and individuals with unilateral GR were excluded. After informing individuals who voluntarily agreed to participate in the study about the study's aims and procedures, the informed consent form was read and signed. One month after completion of the phase 1 periodontal treatments, 20 patients were randomly divided into two groups, right and left, according to a split-mouth study design (Fig. 1 ). Group 1 (Test group): CAF + HA (0.5 ml 0.8% HA gel) (n = 46) Group 2 (Control group): CAF + placebo (n = 40 ) Primary and secondary outcome variables The primary outcome variable was complete root coverage. Secondary outcome variables included mean root coverage (MRC), clinical attachment level (CAL), vertical recession depth (VRD), horizontal recession width (HRW), gingival thickness, attached gingival width (AGW), and keratinized tissue width (KTW), probing pocket depth (PPD), patient aesthetic score, patient comfort score (PCS), and patient sensitivity score (PSS). Clinical measurements and intra-examiner reproducibility Clinical measurements and patient satisfaction were recorded on the day of surgery, before the procedure. Periodontal examinations were performed by the same researcher (FÜ) using a periodontal probe (University of North Carolina 15, Hufriedy ®, Chicago, IL, USA). To assess plaque accumulation and severity, the plaque index (PI) [ 20 ]; (ii) to assess gingival inflammation, the gingival index (GI) [ 21 ], and probing pocket depth (PPD); (iii) to measure the dimensions of the recession defect, the CAL, VRD, and HRW; to evaluate the gingival phenotype, the AGW, KGW, and GT were recorded. To standardize probe placement and angulation during measurement, an acrylic stent with a vertical groove was fabricated for each surgical site. PPD, CAL, VRD, and KGW were recorded from the mid-buccal aspect of the treated teeth. VRD was measured from the CEJ to the free gingival margin. CAL was measured from a fixed reference point (CEJ) to the sulcus base. KGW was measured from the mucogingival junction (MGJ) to the gingival margin. GT was measured 2 mm apical to the gingival margin under local anesthesia using an endodontic spreader with a stopper [ 22 ] and then evaluated by transferring the thickness to an electronic digital caliper (Stainless Steel Digital Caliper, HY818, Shan, China). CRC and MRC were calculated according to the following standard formulas. The root coverage percentage was calculated from VRD measurements taken 6 months before and after surgery using the following formula and expressed as a percentage [ 4 ]. The percentage of CRC was determined by dividing the number of teeth with completely covered exposed root surfaces at 6 months post-treatment by the total number of teeth treated and expressed as a percentage [ 4 ]. Percentage of root coverage: [ (post-op.VRD – pre-op.VRD) / pre-op.VRD] ×100 Percentage of complete root coverage: [(teeth with complete root coverage) / (all treated teeth)] × 100 Cohen's kappa coefficient was used to test the intra-examiner agreement. A researcher repeated measurement sessions at 2-time points for a total of 10 patients not involved in the study, and measurements of PPD and CAL. The contingency coefficients for PPD and CAL were 0.92 and 0.96, respectively. Patient satisfaction analysis Patient satisfaction was assessed using a visual analog scale (VAS) to evaluate comfort, sensitivity, and aesthetic parameters from the patient's perspective, and the analysis was repeated 6 months after surgery. The patient comfort score was determined by asking patients to rate their pain, swelling, and other experiences during and after surgical procedures on a scale ranging from "unbearable discomfort (score 0)" to "no discomfort (score 10)". The patient aesthetic score was determined by asking the patient to rate the color, appearance, and form of the treated area, using the following scale: "poor-unexpected aesthetics (score 0) → beautiful-perfect aesthetics (score 10)". The patient sensitivity score was determined by covering the adjacent teeth with gloved fingers, blowing air onto the treated root surface, and asking the patient about the pain/discomfort they felt, ranging from "painless discomfort no (score 0) → unbearable pain (score 10)". Patients were also asked to rate the pain they felt over 9 days, starting from the day of the operation as day 1 (most severe pain 10 - no pain 0), bleeding (bleeding present +, no bleeding-), the amount of pain medication taken, and the amount of edema/swelling (rated from 0 to 10) on the case follow-up form and to give it to the relevant researcher at the control appointment on the 14th day. Sample size calculation The required sample size for our study was determined using the G*Power 3.1.9.2 statistical program. Based on the power analysis results for the significant difference in GR reported by Pilloni et al. 23 , it was determined that 16 patients per group were needed to achieve 95% confidence intervals and 95% test power, using group means and standard deviations. To minimize potential complications, the study was planned to enroll 20 individuals. Surgical Procedure Baseline clinical measurements were obtained on the day of the surgical procedure. Before the surgical procedure, a coin toss was used to randomly assign one of the bilateral maxillary recession sites to the test group and the other to the control group (Fig. 2 A, Fig. 3 A). All surgical procedures were performed by the same researcher (FÜ). After applied local anesthesia with 2% articaine containing 1: 1,000,000 epinephrine, sulcular incisions were made on the teeth using a No. 15C scalpel blade, and horizontal incisions connecting the interdental areas were made slightly coronal to the CEJ in the interdental areas. After two vertical incisions extending to the apical end of the MGJ were made on the mesio-facial and disto-facial edges of the teeth. A trapezoidal mucoperiosteal flap was elevated to the level of the MGJ using blunt dissection. A half-thickness flap was lifted at the level of the MGJ using a scalpel, and sharp dissection was advanced apically until the flap could be positioned coronally and could rest passively at the CEJ without any tension [ 24 ]. All papillae were de-epithelialized to create a connective tissue bed. Plaque, calculus, and soft dental structures on the exposed root surfaces were removed using Gracey curettes (Hu Friedy, Chicago, IL, USA), followed by root surface smoothing and irrigation with physiological saline. No other mechanical or chemical root surface treatment was performed. These procedures were performed identically in both test and control areas during the same session (Fig. 2 B, Fig. 3 B). In the test areas, after root surface preparation, 0.8% HA gel (Gengigel Oral Forte Gel, Ricefarma, Milan, Italy) was applied to the entire area of the retraction defect at the CEJ of the relevant teeth using a sterile instrument (Fig. 2 C). No additional procedures were performed in the control areas. Then, in both areas, the flaps were passively positioned 1–2 mm above the CEJ and sutured with 5/0 PTFE sutures (Doğsan Surgical Sutures, Trabzon, Türkiye). The flaps were secured with horizontal suspension sutures and intermittent simple sutures placed in vertical incisions and were also supported by simple sutures applied to the interproximal areas. Postoperative procedure All individuals were advised not to brush the surgical areas for 2 weeks during the postoperative period, until the sutures were removed, to eat warm, soft foods, and to avoid actively using their oral muscles. Patients were asked to record any pain, bleeding, or swelling they experienced for 9 days after surgery on a follow-up form. Systemic antibiotics (amoxicillin 1 g b.i.d.) were prescribed for 5 days after surgery, and non-steroidal anti-inflammatory analgesic tablets were prescribed 2x1 to be used if needed. Additionally, during this period, they were advised to gargle twice daily with a 0.12% chlorhexidine solution for chemical plaque control. Sutures were removed after 14 days (Fig. 3 C). Patients were called for a follow-up 6 months after suture removal, and clinical measurements and patient satisfaction questionnaires were repeated (Fig. 2 D, Fig. 3 D). Furthermore, supragingival calculus removal and polishing were performed when deemed necessary during follow-up visits. Statistical Analysis SPSS (Statistical Package for the Social Sciences) version 24.0 was used for statistical analyses. Descriptive statistical methods (mean, standard deviation, median, frequency, proportion, minimum, maximum) were used to evaluate the study data, along with the Independent-samples t-test and the Pearson Chi-Square test for group comparisons. The Paired Sample T test, Friedman Halton test, and Cochran's Q Test were used to compare changes observed over time. Significance was assessed at P < 0.01 and P < 0.05 levels. RESULTS Evaluation of Clinical Parameters Intra-group and inter-group statistical comparisons of clinical parameters at baseline and 6 months after surgical treatment in the test and control groups are presented in Table 2. Table 2 Descriptive statistics of the clinical parameters measured at baseline and 6 months after surgery PI Control group (n:46) Test group (n:40) a P Baseline 0.24 ± 0.33 0.16 ± 0.23 0.193 6 months 0.29 ± 0.35 0.24 ± 0.4 0.550 P 0.206 0.449 GI Baseline 0.39 ± 0.41 0.35 ± 0.42 0.703 6 months 0.51 ± 0.52 0.49 ± 0.45 0.183 P 0.006** 0.249 PPD Baseline 2.15 ± 0.47 2.14 ± 0.48 0.933 6 months 1.91 ± 0.34 1.87 ± 0.31 0.538 P 0.001** 0.001** CAL Baseline 4.33 ± 0.87 4.55 ± 0.97 0.266 6 months 2.68 ± 0.94 2.63 ± 0.93 0.826 P 0.001** 0.001** VRD Baseline 2.18 ± 0.61 2.4 ± 0.83 0.155 6 months 0.75 ± 0.74 0.75 ± 0.84 0.996 P 0.001** 0.001** HRW Baseline 3.8 ± 1.44 4 ± 1.25 0.493 6 months 1.8 ± 1.64 1.77 ± 1.65 0.937 P 0.001** 0.001** GT Baseline 1.18 ± 0.23 1.2 ± 0.32 0.735 6 months 1.6 ± 0.34 1.59 ± 0.34 0.833 P 0.001** 0.001** AGW Baseline 0.98 ± 0.78 0.80 ± 0.58 0.238 6 months 1.85 ± 1.02 1.63 ± 0.76 0.259 P 0.001** 0.001** KGW Baseline 3.13 ± 0.79 2.93 ± 0.7 0.229 6 months 3.76 ± 1.02 3.5 ± 0.8 0.185 P 0.001** 0.001** Data are expressed as mean ± standard deviation. P < 0.05 was considered statistically significant. PI: Plaque Index, GI: Gingival Index, PPD: Probing Pocket Depth, CAL: Clinical Attachment Level, VRD: Vertical Recession Depth, HRW: Horizontal Recession Width, GT: Gingival Thickness, AGW: Attached Gingival Width, KGW: Keratinized Gingival Width. *There is a significant difference when comparing pre- and post-treatment data (PairedSample Test ** P < 0.01, * P < 0.05). *There is a significant difference when comparing test and control group data ( a Independent Sample T test) When comparing PI values in the test and control groups before treatment and at 6 months after treatment, an increase was observed at 6 months after treatment compared with baseline levels, but the difference was not statistically significant ( P > 0.05). When GI values were compared, no statistically significant increase was observed in the test group at 6 months post-treatment compared with pre-treatment ( P > 0.05), whereas this increase was significant in the control group ( P = 0 .006). When comparing PPD, CAL, VRD, and HRW values in the test and control groups before treatment and 6 months after surgical treatment, a statistically significant decrease was observed at 6 months after treatment compared to baseline levels ( P = 0.001), while GT, AGW, and KGW showed a statistically significant increase at 6 months post-treatment compared to baseline levels ( P = 0.001). When comparing the clinical parameters of the test and control groups before treatment and 6 months after surgical treatment, no statistically significant differences were found in PI, GI, PPD, CAL, VRD, HRW, GT, AGW, or KGW ( P > 0.05). CRC and MRC The comparison of root closure rates between the test and control groups is shown in Fig. 4. The mean root closure rates were 70.26 ± 29.92% in the test group and 68.78 ± 31.36% in the control group. When comparing the groups, no statistically significant difference in mean root closure rates was found ( P > 0.05). At 6 months, the complete root closure rates were 41.3% in the test group and 42.5% in the control group. No significant difference in complete root closure rates was found between the groups ( P > 0.05). Patient-Based Data Evaluation Pain, bleeding, edema presence, and the number of analgesics used during the first 9 days post-surgery were compared inter- and intra-group in Fig. 5. In intra-group evaluations, statistically significant decreases in bleeding, pain, edema, and analgesic use were observed in both groups during the 9 days after surgery ( P = 0.001). Between-group evaluations showed no statistically significant differences in pain, bleeding, edema, or analgesic use ( P > 0.05). When the severity of pain and edema perceived by the patients was evaluated, statistically significant reductions were observed in both the control and test groups during the 9 days following surgery ( P = 0.001) (Table 3). However, when the severity of pain and edema was compared between the groups, no significant differences were found ( P > 0.05) (Table 3). Patient satisfaction analysis results When the patient satisfaction analysis data were evaluated, statistically significant increases in PCS and PAS scores and a significant decrease in PSS scores were observed in both the test and control groups at 6 months after surgical treatment, compared with baseline ( P = 0.001). In the intergroup comparison, no statistically significant difference in PAS and PSS scores was observed before or after surgical treatment ( P > 0.05). On the other hand, although no significant difference was observed in PCS scores at baseline ( P > 0.05), a statistically significantly greater increase was found in the test group compared to the control group at 6 months postoperative treatment ( P = 0.001) (Table 4). Discussion This study is the first to evaluate the effect of HA on both clinical improvements and patient comfort and satisfaction in the surgical treatment of gingival recession. The split-mouth study found that when CAF was applied with or without HA in the surgical treatment of gingival recession, positive improvements were observed in clinical parameters (PPD, CAL, VRD, HRW, KGW, AGW, GT) and patient satisfaction analysis (PCS, PAS, and PSS) compared to baseline data. Furthermore, the study showed that while HA had no additional effect on clinical improvements at 6 months in the treatment of GR, it had a significantly positive effect on patient comfort. In our study, although no significant differences were observed between the groups in baseline and 6-month VRD, HGR, and CAL values, we analyzed changes in defect size and observed significant improvements in both groups at 6 months post-surgery compared to baseline. The VRD, HRW, and CAL data of the control group in our study are consistent with studies comparing CAF alone to CAF with additional biomaterials in control groups [ 25 , 26 ]. In their study, Badge et al. 17 , divided 45 patients into three groups and treated them with CAF, CAF + HA (0.2% gengigel), and CAF+membrane techniques. At the 6-month follow-up, although no differences were observed between groups in VRD, HGR, CAL, and PPD values, improvement was observed in all groups compared with baseline [ 17 ]. Additionally, Rajan et al. 27 compared CAF + HA (test) with CAF + CTG (control) and reported that at 9 months post-surgery follow-up, there was a significant difference in favor of the test group only in CAL values, while there was no significant difference in VRD, HRW, and PPD compared to the control group. In a study by Kumar et al. 11 , which had a similar design to ours, 0.2% HA (Gengigel) was applied to the test group, and although no significant difference was found between the groups, they reported positive improvements from baseline to 6 months. Similarly, in their 18-month follow-up parallel technique study, in which they applied CAF and CAF + HA in Miller I defects, Pilloni et al. 23 reported a significant difference in VRD and CAL values in favor of the test group at 18 months post-surgery, while there was no difference in PPD. They also showed significant improvements in VRD, PPD, and CAL values in both the test and control groups. Another study, in its 6-month follow-up, reported no significant difference in VRD and CAL levels between groups, but significant improvements in both groups within each group [ 28 ]. Although our study included different control groups, its findings are consistent with those of these studies. Our study demonstrates that CAF reduces the size of gingival recession defects but suggests that HA does not have an additional effect on the healing process. The study found that, while there were no significant differences between the groups in GI, when evaluated individually, the control group showed a significant increase at 6 months, whereas no significant differences were observed in the test group. Rajan et al. 27 applied CAF + HA and CAF + CTG to Miller I-II gingival defect areas in their 9-month follow-up study and found no significant differences between groups in GI data, but a significant decrease in both groups within each group. The reason for this difference may be the use of additional grafts in the control group. Based on the GI findings of our study, we can suggest that HA has a supportive role in reducing gingival inflammation. Gingival phenotype affects the long-term stability of soft tissues after surgery and increases the rates of thick gingiva MRC and CRC [ 29 ]. Therefore, although clinicians primarily aim for CRC in GR treatments, they also aim to increase GT and KGW. It has been emphasized that bilaminar techniques involving FGG and CTG provide the best increase in GT and KGW when these are insufficient [ 30 ]. On the other hand, a randomized controlled study found that CAF applied alone when baseline GT was > 0.8 mm yielded results similar to those with CAF combined with CTG [ 5 ]. Indeed, based on this information, our study selected patients with GT > 0.8 and evaluated the effectiveness of HA with CAF, aiming to eliminate the discomfort and complications that CTG can cause. In the current study, GT in the test group increased from an initial mean of 1.2 ± 0.32 mm to 1.59 ± 0.34 mm at 6 months post-surgery, while in the control group, it increased from 1.18 ± 0.23 mm to 1.6 ± 0.34 mm. KGW increased from an average of 2.93 ± 0.7 mm to 3.5 ± 0.8 mm in the test group and from 3.13 ± 0.79 mm to 3.76 ± 1.02 mm in the control group. In our study, no significant difference was found between the groups for GT, KGW, and AGW, while significant increases were observed in both groups during the follow-up period. The increase in keratinized tissue after CAF can be explained by the tendency to return to the genetically determined position of the MGJ [ 31 ]. Our findings regarding KGW were parallel to the few existing studies on HA application in GR treatments [ 23 , 27 ]. However, these studies did not adequately evaluate GT [ 11 , 23 , 27 ]. Gorski et al. 32 , in their study evaluating the HA effect in addition to tunnel + CTG, emphasized that although KGW and GT values increased over the follow-up period, they did not detect any differences between the groups. These data are consistent with our study. In the current study, the MRC in the test group was 70.26 ± 29.92%, while in the control group it was 68.78 ± 31.36%; the difference between the groups was not statistically significant. The MRC in the control group is consistent with the root coverage reported in the control groups of the studies by Silva et al. 33 and Woodyard et al. 7 The average root coverage in the test group in the current study was similar to that reported by Kumar et al. 11 Furthermore, the authors reported CRC values at 6 months as 40% and 20% for the test and control groups, respectively [ 11 ], and it was observed that the test group's values were very close to the CRC of the test group in our study (19 out of 46 defects; 41.3%). Furthermore, the CRC in our control group was 42.5% (17 out of 40 defects). However, apart from the CRC of the control group in the current study, the total and MRC scores were also lower than those reported by Pilloni et al. 23 in the study using the parallel technique. In their study, Rajan et al. 27 planned CAF + HA (test) and CAF + CTG (control) in a divided-mouth design and reported CRCs of 77.84% and 82.15%, respectively, in 20 patients. Although the MRC in the test group was similar to that in the current study, the high MRC observed in the control group is thought to be related to CTG [ 27 ]. However, the percentage of HA (gengigel) used in the study and the complete root coverage scores were not specified [ 27 ]. In our study, patient comfort in the postoperative period was evaluated by monitoring pain, edema, and bleeding scores, as well as the number of painkillers patients reported using over 9 days. According to the study results, 18 out of 20 patients reported feeling pain on the first day after surgery, and 14 of these patients reported needing painkillers. Furthermore, although the most severe pain scores recorded on the first day were slightly higher in the control group than in the test group (4.89 ± 2.87, 4.56 ± 2.62), this difference was not statistically significant on subsequent days. Although there was no statistically significant difference in pain intensity between the groups on the following days, regular and significant decreases were determined within the group daily. On the second day, 6 patients; on the third day, 5 patients; and on the fourth day, only 3 out of 20 patients reported feeling the need to use painkillers. The amount of analgesic used decreased gradually over the follow-up days but did not differ between the groups. When bleeding parameters were examined, 10 patients in each group reported bleeding on the 1st day; however, leakage-type bleeding in the first days after surgery was considered normal due to the dynamic structure of the mouth. Although bleeding, edema, pain scores, and the amount of pain medication used, which could be used as indicators of patient comfort and return to daily routine in the postoperative period, showed small differences between the groups, these differences were not statistically significant. Only one study in the literature applied HA for the treatment of GR and evaluated the postoperative period [ 23 ]. In their study, Pilloni et al. 23 divided 30 patients into two groups and applied CAF + HA(test) and CAF alone (control) and evaluated the patients' pain, edema and daily comfort over 7 days and found no difference between the groups in terms of pain parameters, while the test group showed significantly better results in terms of comfort and edema [ 23 ]. However, unlike the present study, this study did not use a split-mouth design, used a different HA biomaterial (cross-linked HA 16 mg/ml), and the pain felt and edema reported by patients were subjective parameters, making it impossible to compare with our data. Furthermore, in our study, consistent with previous studies [34, 35], HA use was well tolerated and did not cause any immunological complications or toxic effects among patients who completed their treatments. Based on the results of the current study, we believe that the additional use of HA in CAF treatment for GR does not cause greater discomfort in the postoperative period than placebo. In the current study, patient satisfaction was analyzed by comparing PCS, PSS, and PAS scores before treatment and at 6 months. When all three parameters were evaluated, significant improvements were reported in 6 months compared to baseline levels within the group. When comparing groups, no significant difference was found between baseline and 6-month data for PAS and PSS, whereas PCS values at 6 months showed significantly better results in the test group than in the control group. In the study by Gorski et al. 28 , which evaluated the effectiveness of HA in addition to tunnel + CTG, an aesthetic evaluation was performed; however, unlike in the current study, it was not patient-oriented. It was performed by an independent periodontist, who evaluated indicators such as soft tissue, marginal tissue contour, root coverage amounts, and color mismatch, and only soft tissue showed positive results in favor of the test group [ 28 ].In a case series, 15 patients were treated with modified coronally advanced tunnel (MCAT) + CTG+HA, and a high MRC (85%) was reported [ 36 ]. An independent observer rated the aesthetic score based on photographs, particularly considering the degree of root coverage, as 7.9 out of 10 [ 36 ]. The aesthetic score in the current study was patient-reported, with values of 7.95 for the control group and 8.43 for the test group. No significant difference was found between the groups, and the values were similar to those reported by Lanzrein et al. 36 However, due to the absence of a control group in this study, the evaluation was not patient-oriented, and the surgical procedure differed from that in the current study, making it impossible to reach a clear conclusion. Conclusion In conclusion, our study demonstrated that both CAF and the additional application of HA to CAF were successful in covering the root surface in the treatment of multiple RT1. It also determined that additional HA application to CAF may be beneficial for reducing gingival inflammation in periodontal treatments for GR. Moreover, it suggests that the use of HA in addition to CAF may improve patient comfort scores in the GR. However, randomized, well-designed, and histological evaluation studies are needed in the future to better explain the mechanisms of action of HA in GR treatments. Declarations Author Contributions FÜ and FOD contributed to data acquisition, analysis, and interpretation and drafted the manuscript and also contributed to the conception, design, data analysis, and interpretation and critically revised the manuscript. All authors gave their final approval and agreed to be accountable for all aspects of the work. Data Availability Data were available on request from the corresponding author upon reasonable request. Ethics approval The study was approved by the Clinical Research Ethics Committee of Ondokuz Mayıs University, Samsun, Turkey (protocol number 2024/178) and registered in the Turkish Ministry of Health’s Turkish Medicines and Medical Devices Agency (Approval Number: 2024-031). All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and national research committee and with the Helsinki declaration of 1975, as revised in 2013 and its later amendments or comparable ethical standards. Consent to participate The patients were informed about the nature of the study and informed consent was obtained from all individual participants included in the study. Competing interests The authors declare no competing interests. References Goldstein M, Nasatzky E, Goultschin J, Boyan BD, Schwartz Z (2002) Coverage of previously carious roots is as predictable a procedure as coverage of intact roots. J Periodontol 73:1419–1426. https://doi.org/10.1902/jop.2002.73.12.1419 Cairo F, Nieri M, Pagliaro U (2014) Efficacy of periodontal plastic surgery procedures in the treatment of localized facial gingival recessions: a systematic review. J Clin Periodontol 41:S44–S62. https://doi.org/10.1111/jcpe.12182 Zucchelli G, Tavelli L, Ravidà A, et al. (2018) Influence of tooth location on coronally advanced flap procedures for root coverage. J Periodontol 89:1428–1441. https://doi.org/10.1002/JPER.18-0201 Eren G, Atilla G (2014) Platelet-rich fibrin in the treatment of localized gingival recessions: a split-mouth randomized clinical trial. Clin Oral Investig 18:1941–1948. https://doi.org/10.1007/s00784-013-1170-5 Cairo F, Cortellini P, Pilloni A, et al. (2016) Clinical efficacy of coronally advanced flap with or without connective tissue graft for the treatment of multiple adjacent gingival recessions in the aesthetic area: a randomized controlled clinical trial. J Clin Periodontol 43:849–856. https://doi.org/10.1111/jcpe.12590 Sculean A, Schwarz F, Becker J, Brecx M (2007) The application of an enamel matrix protein derivative (Emdogain®) in regenerative periodontal therapy: a review. Med Princ Pract 16:167–180. https://doi.org/10.1159/000100386 Woodyard JG, Greenwell H, Hill M, et al. (2004) The clinical effect of acellular dermal matrix on gingival thickness and root coverage compared to coronally positioned flap alone. J Periodontol 75:44–56. https://doi.org/10.1902/jop.2004.75.1.44 Lorenzo R, García V, Orsini M, Martin C, Sanz M (2012) Clinical efficacy of a xenogeneic collagen matrix in augmenting keratinized mucosa around implants: a randomized controlled prospective clinical trial. Clin Oral Implants Res 23:316–324. https://doi.org/10.1111/j.1600-0501.2011.02260.x Barootchi S, Tavelli L, Vinueza M, et al. (2025) Autologous platelet concentrates in root coverage procedures. Periodontol 2000 97:215–235. https://doi.org/10.1111/prd.12614 Bozkurt Doğan Ş, Öngöz Dede F, Ballı U, et al. (2015) Concentrated growth factor in the treatment of adjacent multiple gingival recessions: a split-mouth randomized clinical trial. J Clin Periodontol 42:868–875. https://doi.org/10.1111/jcpe.12444 Kumar R, Srinivas M, Pai J, et al. (2014) Efficacy of hyaluronic acid in root coverage procedures as an adjunct to coronally advanced flap in Miller class I recession: a clinical study. J Indian Soc Periodontol 18:746. https://doi.org/10.4103/0972-124X.147411 Shirakata Y, Nakamura T, Kawakami Y, et al. (2021) Healing of buccal gingival recessions following treatment with coronally advanced flap alone or combined with a cross-linked hyaluronic acid gel. J Clin Periodontol 48:570–580. https://doi.org/10.1111/jcpe.13433 Vedamurthy M (2004) Soft tissue augmentation—use of hyaluronic acid as dermal filler. Indian J Dermatol Venereol Leprol 70:383–387. PMID:17642675 Keen MA (2017) Hyaluronic acid in dermatology. Skinmed 15:441–448 Casale M, Moffa A, Vella P, et al. (2016) Hyaluronic acid: perspectives in dentistry. Int J Immunopathol Pharmacol 29:572–582. https://doi.org/10.1177/0394632016652906 Kalimeri E, Roccuzzo A, Stähli A, et al. (2024) Adjunctive use of hyaluronic acid in the treatment of gingival recessions: a systematic review and meta-analysis. Clin Oral Investig 28:329. https://doi.org/10.1007/s00784-024-05701-7 Bagde H, Pawar SK, Vasisth D, et al. (2023) Comparison of amnion membrane and hyaluronic acid in gingival recession coverage and gain in clinical attachment level following coronally advanced flap procedure—a clinical study.J Pharm Bioallied Sci 15(Suppl 2):S1104–S1107.https://doi.org/10.4103/jpbs.jpbs_202_23 Jepsen S, Caton JG, Albandar JM, et al. (2018) Periodontal manifestations of systemic diseases and developmental and acquired conditions. J Periodontol 89:S237–S248. https://doi.org/10.1002/JPER.17-0733 Cairo F, Nieri M, Cincinelli S, Mervelt J, Pagliaro U (2011) The interproximal clinical attachment level to classify gingival recessions and predict root coverage outcomes. J Clin Periodontol 38:661–666. https://doi.org/10.1111/j.1600-051X.2011.01732.x Löe H, Silness J (1963) Periodontal disease in pregnancy I. Prevalence and severity. Acta Odontol Scand 21:533–551. https://doi.org/10.3109/00016356309011240 Silness J, Löe H (1964) Periodontal disease in pregnancy II. Correlation between oral hygiene and periodontal condition. Acta Odontol Scand 22:121–135. https://doi.org/10.3109/00016356408993968 Soltani P, Yaghini J, Rafiei K, et al. (2023) Comparative evaluation of the accuracy of gingival thickness measurement by clinical evaluation and intraoral ultrasonography. J Clin Med 12:4395. https://doi.org/10.3390/jcm12134395 Pilloni A, Schmidlin PR, Sahrmann P, et al. (2019) Effectiveness of adjunctive hyaluronic acid application in coronally advanced flap in Miller class I single gingival recession sites. Clin Oral Investig 23:1133–1141. https://doi.org/10.1007/s00784-018-2537-4 De Sanctis M, Zucchelli G (2007) Coronally advanced flap: a modified surgical approach for isolated recession-type defects. J Clin Periodontol 34:262–268. https://doi.org/10.1111/j.1600-051X.2006.01039.x Del Pizzo M, Zucchelli G, Modica F, Villa R, Debernardi C (2005) Coronally advanced flap with or without enamel matrix derivative for root coverage. J Clin Periodontol 32:1181–1187. https://doi.org/10.1111/j.1600-051X.2005.00831.x Sharma A, Wadhawan A (2022) Comparative evaluation of coronally advanced flap with and without Biomesh® membrane. J Med Life 15:705–716. https://doi.org/10.25122/jml-2021-0109 Rajan P, Rao NM, Nera M, Rahaman SM (2015) Hyaluronon as an adjunct to coronally advanced flap. NJIRM 6(2). Górski B, Szerszeń M, Kaczyński T (2022) Effect of 24% EDTA root conditioning on the outcome of modified coronally advanced tunnel technique. Clin Oral Investig 26:1761–1772. https://doi.org/10.1007/s00784-021-04151-9 Rasperini G, Codari M, Paroni L, et al. (2020) The influence of gingival phenotype on the outcomes of coronally advanced flap. Int J Periodontics Restorative Dent 40:e27–e34. https://doi.org/10.11607/prd.4272 Barootchi S, Tavelli L, Zucchelli G, Giannobile WV, Wang HL (2020) Gingival phenotype modification therapies on natural teeth. J Periodontol 91:1386–1399. https://doi.org/10.1002/JPER.19-0715 Anjcamo A, Bergenholtz A, Hugoson A, Ainamo J (1992) Location of the mucogingival junction 18 years after apically repositioned flap surgery. J Clin Periodontol 19:49–52. https://doi.org/10.1111/j.1600-051x.1992.tb01148.x Górski B, Skierska I, Szerszeń M, Mańka-Malara K (2023) Tunnel technique with cross-linked hyaluronic acid in addition to subepithelial connective tissue graft. Clin Oral Investig 27:2395–2406. https://doi.org/10.1007/s00784-023-04887-6 Silva RC, Joly JC, de Lima AF, Tatakis DN (2004) Root coverage using the coronally positioned flap. J Periodontol 75:413–419. https://doi.org/10.1902/jop.2004.75.3.413 Mamajiwala AS, Sethi KS, Raut CP, et al. (2021) Clinical and radiographic evaluation of 0.8% hyaluronic acid as an adjunct to open flap debridement. Clin Oral Investig 25:5257–5271. https://doi.org/10.1007/s00784-021-03834-7 Lanzrein C, Guldener K, Imber JC, et al. (2020) Treatment of multiple adjacent recessions with modified coronally advanced tunnel. Quintessence Int 51:710–719. https://doi.org/10.3290/j.qi.a44808 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9114410","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":609697315,"identity":"1dad7c73-a5c9-4957-99e6-cdff7b8654a3","order_by":0,"name":"Fadime ÜLVAN","email":"","orcid":"","institution":"Ordu University","correspondingAuthor":false,"prefix":"","firstName":"Fadime","middleName":"","lastName":"ÜLVAN","suffix":""},{"id":609697318,"identity":"c8775b1e-be62-4acb-8e91-9ac4e1e4f5d6","order_by":1,"name":"Figen ÖNGÖZ DEDE","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9UlEQVRIiWNgGAWjYBAC9mYEm/ExmGJmbsCrhecwgs1szMBgAKQYCWg5gGCzSYO1MBDSws5+8XEBg02evHv7s+qCij/R/O1ALT8qtuHWwsxTbDyDIa3Y8MyBtNszzhjkzjjM2MDYc+Y2Ti32zDxp0jwMhxM3zkg4dpu3zSC3AaiFmbENtxagLem/IVoS24pBWuYT1sJ+jBmkZb5EMhszSMsGImxhBjosLXEDzzEg44xx7kagloP4/MLDf/zhZx4Gm8T57e1ARoVc7rzzhw8++FGBWwtQkwED4z9ghBxAEjuAQy0UsD8AU/IN+JWNglEwCkbBCAYAu1lR7dkT3c8AAAAASUVORK5CYII=","orcid":"","institution":"Ordu University","correspondingAuthor":true,"prefix":"","firstName":"Figen","middleName":"ÖNGÖZ","lastName":"DEDE","suffix":""}],"badges":[],"createdAt":"2026-03-13 11:53:20","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9114410/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9114410/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":105564858,"identity":"c0bb8567-43e1-4ba5-ad1d-794d1c923a58","added_by":"auto","created_at":"2026-03-27 12:51:07","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":516781,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of the split-mouth randomized clinical study\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-9114410/v1/591578387652accf2ebd1de4.png"},{"id":105565356,"identity":"9191e969-9fb9-4088-b38f-6e54b5636e0f","added_by":"auto","created_at":"2026-03-27 12:53:02","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":690988,"visible":true,"origin":"","legend":"\u003cp\u003eTest group (A): Preoperative clinical view of the surgical site. (B): Flap elevation using the split-full-split thickness technique. (C): Application of 0.8% HA gel to the defect area. (D): Clinical view at the 6th postoperative month\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-9114410/v1/8810392658f52e6eeb4d06ce.png"},{"id":105297478,"identity":"31d01e2a-f3e7-4300-b642-86b32270bcb4","added_by":"auto","created_at":"2026-03-24 13:19:54","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":549520,"visible":true,"origin":"","legend":"\u003cp\u003eControl site (A): Preoperative clinical view of the surgical site. (B): Flap elevation using the split-full-split thickness technique. (C): Clinical view at the 14th-day follow-up appointment. (D): Clinical view at the 6th postoperative month\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-9114410/v1/cec7ae2be20bb358fa108327.png"},{"id":105564710,"identity":"b30e883c-259f-4189-b464-d2d3abf6ed82","added_by":"auto","created_at":"2026-03-27 12:50:35","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":6728,"visible":true,"origin":"","legend":"\u003cp\u003eComparison graph of 6th month means and complete root closure rates in the test and control groups.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-9114410/v1/b4fbe263c9d195b7e6e044ab.png"},{"id":105565030,"identity":"6f193a23-ef11-4a88-93f7-7d93f78a2af3","added_by":"auto","created_at":"2026-03-27 12:51:38","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":37384,"visible":true,"origin":"","legend":"\u003cp\u003ePost-surgical pain, bleeding, edema scores and the number of analgesics used graph of the test and control groups\u003c/p\u003e\n\u003cp\u003eSignificant decreases in pain scores, bleeding, edema scores and the number of analgesics used were observed in both groups compared to baseline.\u003c/p\u003e\n\u003cp\u003e*The difference between the groups was not significant.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-9114410/v1/1b6e254f0ca10d37678a06ed.png"},{"id":105728012,"identity":"7200b8d1-e669-4a11-a768-0a065fd83192","added_by":"auto","created_at":"2026-03-30 11:08:02","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3492556,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9114410/v1/23f06fb0-27ac-48a4-94c0-c4fa5d9152d7.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eClinical Efficacy and Patient Experience of Adjunctive Hyaluronic Acid-based Gel Application With Coronally Advanced Flap in Type 1 Gingival Recession: A Randomized Controlled Clinical Trial\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eGingival recession (GR) causes cervical dentin sensitivity, plaque accumulation, and gingival inflammation, root caries, aesthetic concerns, and psychological problems, such as the patient's fear of losing their teeth [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The literature considers the application of connective tissue grafting (CTG) with a coronally advanced flap (CAF) to be the gold standard among surgical procedures for root coverage [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. However, CTG has disadvantages such as the need for a donor site, increased patient morbidity, postoperative bleeding, and insufficient tissue acquisition [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Furthermore, Cairo et al.\u003csup\u003e5\u003c/sup\u003e reported that CAF applied alone in cases where gingival thickness (GT)\u0026thinsp;\u0026gt;\u0026thinsp;0.8 mm yielded results complete root coverage (CRC) and reduction in recession depth similar to those obtained with CAF applied in combination with CTG. Thus, researchers have sought different materials for surgical treatment of GR, such as enamel matrix derivatives [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], acellular dermal matrix [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], xenogeneic collagen matrix [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], platelet-rich plasma [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], platelet-rich fibrin [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]; concentrated growth factors [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], and more recently, hyaluronic acid (HA) [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], have emerged as biomaterials and alternative grafts.\u003c/p\u003e \u003cp\u003eHyaluronic acid is a branched, single-chain polysaccharide that is endogenously present in all living organisms and plays a vital role in the extracellular spaces of body tissues [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. In wound-healing and tissue-repair processes, HA, an endogenous compound, has been shown to play an active role by maintaining a moist environment conducive to healing and by stimulating the migration of growth factors, cellular components, and various cells necessary for healing [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Furthermore, Casale et al.\u003csup\u003e15\u003c/sup\u003e reported that topically applied hyaluronic acid could be used as an adjunctive treatment to reduce patient discomfort and accelerate healing in the postoperative period following implant and sinus lift procedures, as well as for the treatment of gingivitis and chronic periodontitis. The investigation of HA efficacy in the treatment of GR is relatively new compared to other biomaterials used in this field [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. In an animal study investigating the histological outcomes of HA use in the treatment of GR, the authors reported that when CAF was applied in combination with cross-linked hyaluronic acid, the test group showed a statistically significant increase in periodontal ligament (PDL)-like tissue and cementum containing new bone compared to the control group treated with CAF alone [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. The researchers also reported that newly formed collagen fibers in the control areas ran parallel to the root surface, but in the test areas, these fibers were inclined toward the root surface [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. A recent clinical study found that CAF\u0026thinsp;+\u0026thinsp;HA (0.2% gengigel) for the treatment of GR did not demonstrate superiority over CAF alone or CAF with membrane techniques on clinical parameters at the 6-month follow-up [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Moreover, a recent meta-analysis sought to determine the effect of HA on the treatment of GR [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. However, a clear conclusion could not be reached due to the limited number of studies in literature, the heterogeneity of the control groups across the three included studies, and differences in follow-up periods [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. The researchers suggested that more controlled randomized trials are needed in this area [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWithin the scope of the available information, a literature review conducted to date has found very limited sources evaluating the use of hyaluronic acid in combination with CAF for the treatment of GR. While these sources frequently evaluate the clinical success of HA in root coverage procedures, its effects on patient experience have not been evaluated. We hypothesized that the additional use of hyaluronic acid in root coverage procedures would increase root coverage clinical success rates and patient aesthetic scores, accelerate wound healing, and reduce postoperative complaints such as pain, discomfort, and bleeding. Thus, the aim of this study is to evaluate the effect of hyaluronic acid-based gel applied with the CAF technique on clinical parameters, postoperative morbidity, and patient aesthetic scores in Type 1 (RT1) gingival recessions.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003eVolunteers for this randomized controlled clinical trial were selected from patients who visited the Periodontology Clinic of Ordu University Faculty of Dentistry between September 2024 and July 2025 due to aesthetic and sensitivity complaints caused by GR. The study protocol was approved by the Ondokuz Mayıs University Clinical Research Ethics Committee, Samsun, T\u0026uuml;rkiye (Approval Number: 2024/178) and deemed appropriate for initiation by the T\u0026uuml;rkiye Ministry of Health\u0026apos;s Medicines and Medical Devices Agency (Approval Number: 2024-031). Furthermore, the study was conducted in accordance with the 1975 Declaration of Helsinki, as revised in 2013. The trial is registered with ClinicalTrials.gov and is identified as NCT07437885.\u003c/p\u003e\n\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003eStudy population and design\u003c/h2\u003e\n \u003cp\u003eAt the start of the study, the GR of participants\u0026apos; assessment was defined according to the criteria for periodontal manifestations of systemic diseases and developmental and acquired conditions outlined in the consensus report of the 3rd working group of the 2017 World Workshop on Classification of Periodontal and Peri-Implant Diseases and Conditions [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e\n \u003cp\u003eThe evaluation of GR in volunteers was defined according to the criteria established by Cairo et al. in 2011\u003csup\u003e19\u003c/sup\u003e. According to these criteria, recession without interproximal attachment loss and where the interproximal cement-enamel junction (CEJ) can\u0026rsquo;t be clinically identified on the distal and mesial sides of the tooth was considered type I recession (RT1). Twenty individuals with bilateral multiple RT1 GR in the maxilla, with an average age of 42.34\u0026thinsp;\u0026plusmn;\u0026thinsp;9.60, including 9 women and 11 men, were included in our study (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The maxillary right and left incisors, canines, premolars, and first molars were included in our study. The individuals included in the study had 4 upper right central, 4 upper right lateral, 12 upper right canine, 12 upper right first premolar, 6 upper right second premolar, 7 upper right first molars, 4 upper left central incisors, 4 upper left lateral incisors, 13 upper left canines, 13 upper left first premolars, 2 upper left second premolars, and 5 upper left first molars, totaling 86 teeth. Individuals meeting the criteria were selected by a single researcher (F\u0026Uuml;) at the Periodontology Clinic of the Faculty of Dentistry, Ordu University. All volunteers included in the study received oral hygiene training, and phase 1 non-surgical periodontal treatments were completed.\u003c/p\u003e\n \u003cp\u003e\u003cimg 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\"\u003e\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003cbr\u003e\u003c/div\u003e\n \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003eParticipants with systemic diseases affecting wound healing, those who had undergone periodontal surgical treatment in the relevant areas, those with active periodontal disease, smokers, pregnant and breastfeeding women, and individuals with unilateral GR were excluded. After informing individuals who voluntarily agreed to participate in the study about the study\u0026apos;s aims and procedures, the informed consent form was read and signed.\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003eOne month after completion of the phase 1 periodontal treatments, 20 patients were randomly divided into two groups, right and left, according to a split-mouth study design (Fig. \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eGroup 1 (Test group): CAF\u0026thinsp;+\u0026thinsp;HA (0.5 ml 0.8% HA gel) (n\u0026thinsp;=\u0026thinsp;46)\u003c/p\u003e\n \u003cp\u003eGroup 2 (Control group): CAF\u0026thinsp;+\u0026thinsp;placebo (n\u0026thinsp;=\u0026thinsp;40\u003cstrong\u003e)\u003c/strong\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003ePrimary and secondary outcome variables\u003c/h3\u003e\n\u003cp\u003eThe primary outcome variable was complete root coverage. Secondary outcome variables included mean root coverage (MRC), clinical attachment level (CAL), vertical recession depth (VRD), horizontal recession width (HRW), gingival thickness, attached gingival width (AGW), and keratinized tissue width (KTW), probing pocket depth (PPD), patient aesthetic score, patient comfort score (PCS), and patient sensitivity score (PSS).\u003c/p\u003e\n\u003ch3\u003eClinical measurements and intra-examiner reproducibility\u003c/h3\u003e\n\u003cp\u003eClinical measurements and patient satisfaction were recorded on the day of surgery, before the procedure. Periodontal examinations were performed by the same researcher (F\u0026Uuml;) using a periodontal probe (University of North Carolina 15, Hufriedy \u0026reg;, Chicago, IL, USA). To assess plaque accumulation and severity, the plaque index (PI) [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]; (ii) to assess gingival inflammation, the gingival index (GI) [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], and probing pocket depth (PPD); (iii) to measure the dimensions of the recession defect, the CAL, VRD, and HRW; to evaluate the gingival phenotype, the AGW, KGW, and GT were recorded. To standardize probe placement and angulation during measurement, an acrylic stent with a vertical groove was fabricated for each surgical site. PPD, CAL, VRD, and KGW were recorded from the mid-buccal aspect of the treated teeth. VRD was measured from the CEJ to the free gingival margin. CAL was measured from a fixed reference point (CEJ) to the sulcus base. KGW was measured from the mucogingival junction (MGJ) to the gingival margin. GT was measured 2 mm apical to the gingival margin under local anesthesia using an endodontic spreader with a stopper [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] and then evaluated by transferring the thickness to an electronic digital caliper (Stainless Steel Digital Caliper, HY818, Shan, China).\u003c/p\u003e\n\u003cp\u003eCRC and MRC were calculated according to the following standard formulas. The root coverage percentage was calculated from VRD measurements taken 6 months before and after surgery using the following formula and expressed as a percentage [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. The percentage of CRC was determined by dividing the number of teeth with completely covered exposed root surfaces at 6 months post-treatment by the total number of teeth treated and expressed as a percentage [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003ePercentage of root coverage:\u003c/p\u003e\n\u003cp\u003e[ (post-op.VRD \u0026ndash; pre-op.VRD) / pre-op.VRD] \u0026times;100\u003c/p\u003e\n\u003cp\u003ePercentage of complete root coverage:\u003c/p\u003e\n\u003cp\u003e[(teeth with complete root coverage) / (all treated teeth)] \u0026times; 100\u003c/p\u003e\n\u003cp\u003eCohen\u0026apos;s kappa coefficient was used to test the intra-examiner agreement. A researcher repeated measurement sessions at 2-time points for a total of 10 patients not involved in the study, and measurements of PPD and CAL. The contingency coefficients for PPD and CAL were 0.92 and 0.96, respectively.\u003c/p\u003e\n\u003ch3\u003ePatient satisfaction analysis\u003c/h3\u003e\n\u003cp\u003ePatient satisfaction was assessed using a visual analog scale (VAS) to evaluate comfort, sensitivity, and aesthetic parameters from the patient\u0026apos;s perspective, and the analysis was repeated 6 months after surgery. The patient comfort score was determined by asking patients to rate their pain, swelling, and other experiences during and after surgical procedures on a scale ranging from \u0026quot;unbearable discomfort (score 0)\u0026quot; to \u0026quot;no discomfort (score 10)\u0026quot;. The patient aesthetic score was determined by asking the patient to rate the color, appearance, and form of the treated area, using the following scale: \u0026quot;poor-unexpected aesthetics (score 0) \u0026rarr; beautiful-perfect aesthetics (score 10)\u0026quot;. The patient sensitivity score was determined by covering the adjacent teeth with gloved fingers, blowing air onto the treated root surface, and asking the patient about the pain/discomfort they felt, ranging from \u0026quot;painless discomfort no (score 0) \u0026rarr; unbearable pain (score 10)\u0026quot;.\u003c/p\u003e\n\u003cp\u003ePatients were also asked to rate the pain they felt over 9 days, starting from the day of the operation as day 1 (most severe pain 10 - no pain 0), bleeding (bleeding present +, no bleeding-), the amount of pain medication taken, and the amount of edema/swelling (rated from 0 to 10) on the case follow-up form and to give it to the relevant researcher at the control appointment on the 14th day.\u003c/p\u003e\n\u003ch3\u003eSample size calculation\u003c/h3\u003e\n\u003cp\u003eThe required sample size for our study was determined using the G*Power 3.1.9.2 statistical program. Based on the power analysis results for the significant difference in GR reported by Pilloni et al.\u003csup\u003e23\u003c/sup\u003e, it was determined that 16 patients per group were needed to achieve 95% confidence intervals and 95% test power, using group means and standard deviations. To minimize potential complications, the study was planned to enroll 20 individuals.\u003c/p\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003eSurgical Procedure\u003c/h2\u003e\n \u003cp\u003eBaseline clinical measurements were obtained on the day of the surgical procedure. Before the surgical procedure, a coin toss was used to randomly assign one of the bilateral maxillary recession sites to the test group and the other to the control group (Fig. \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA, Fig. \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). All surgical procedures were performed by the same researcher (F\u0026Uuml;).\u003c/p\u003e\n \u003cp\u003eAfter applied local anesthesia with 2% articaine containing 1: 1,000,000 epinephrine, sulcular incisions were made on the teeth using a No. 15C scalpel blade, and horizontal incisions connecting the interdental areas were made slightly coronal to the CEJ in the interdental areas. After two vertical incisions extending to the apical end of the MGJ were made on the mesio-facial and disto-facial edges of the teeth. A trapezoidal mucoperiosteal flap was elevated to the level of the MGJ using blunt dissection. A half-thickness flap was lifted at the level of the MGJ using a scalpel, and sharp dissection was advanced apically until the flap could be positioned coronally and could rest passively at the CEJ without any tension [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. All papillae were de-epithelialized to create a connective tissue bed. Plaque, calculus, and soft dental structures on the exposed root surfaces were removed using Gracey curettes (Hu Friedy, Chicago, IL, USA), followed by root surface smoothing and irrigation with physiological saline. No other mechanical or chemical root surface treatment was performed. These procedures were performed identically in both test and control areas during the same session (Fig. \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB, Fig. \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). In the test areas, after root surface preparation, 0.8% HA gel (Gengigel Oral Forte Gel, Ricefarma, Milan, Italy) was applied to the entire area of the retraction defect at the CEJ of the relevant teeth using a sterile instrument (Fig. \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). No additional procedures were performed in the control areas. Then, in both areas, the flaps were passively positioned 1\u0026ndash;2 mm above the CEJ and sutured with 5/0 PTFE sutures (Doğsan Surgical Sutures, Trabzon, T\u0026uuml;rkiye). The flaps were secured with horizontal suspension sutures and intermittent simple sutures placed in vertical incisions and were also supported by simple sutures applied to the interproximal areas.\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003ePostoperative procedure\u003c/h3\u003e\n\u003cp\u003eAll individuals were advised not to brush the surgical areas for 2 weeks during the postoperative period, until the sutures were removed, to eat warm, soft foods, and to avoid actively using their oral muscles. Patients were asked to record any pain, bleeding, or swelling they experienced for 9 days after surgery on a follow-up form. Systemic antibiotics (amoxicillin 1 g b.i.d.) were prescribed for 5 days after surgery, and non-steroidal anti-inflammatory analgesic tablets were prescribed 2x1 to be used if needed. Additionally, during this period, they were advised to gargle twice daily with a 0.12% chlorhexidine solution for chemical plaque control. Sutures were removed after 14 days (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). Patients were called for a follow-up 6 months after suture removal, and clinical measurements and patient satisfaction questionnaires were repeated (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD). Furthermore, supragingival calculus removal and polishing were performed when deemed necessary during follow-up visits.\u003c/p\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003eStatistical Analysis\u003c/h2\u003e\n \u003cp\u003eSPSS (Statistical Package for the Social Sciences) version 24.0 was used for statistical analyses. Descriptive statistical methods (mean, standard deviation, median, frequency, proportion, minimum, maximum) were used to evaluate the study data, along with the Independent-samples t-test and the Pearson Chi-Square test for group comparisons. The Paired Sample T test, Friedman Halton test, and Cochran\u0026apos;s Q Test were used to compare changes observed over time. Significance was assessed at \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01 and \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 levels.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec12\"\u003e\n \u003ch2\u003eEvaluation of Clinical Parameters\u003c/h2\u003e\n \u003cp\u003eIntra-group and inter-group statistical comparisons of clinical parameters at baseline and 6 months after surgical treatment in the test and control groups are presented in Table 2.\u0026nbsp;\u003c/p\u003e\n \u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 2\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eDescriptive statistics of the clinical parameters measured at baseline and 6 months after surgery\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\n \u003cp\u003ePI\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eControl group (n:46)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eTest group (n:40)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e\u003csup\u003ea\u003c/sup\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eBaseline\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e0.24\u0026thinsp;\u0026plusmn;\u0026thinsp;0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.16\u0026thinsp;\u0026plusmn;\u0026thinsp;0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e\u003cem\u003e0.193\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e6 months\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e0.29\u0026thinsp;\u0026plusmn;\u0026thinsp;0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.24\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e\u003cem\u003e0.550\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e\u003cem\u003e0.206\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e\u003cem\u003e0.449\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cstrong\u003eGI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eBaseline\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e0.39\u0026thinsp;\u0026plusmn;\u0026thinsp;0.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.35\u0026thinsp;\u0026plusmn;\u0026thinsp;0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e\u003cem\u003e0.703\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e6 months\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e0.51\u0026thinsp;\u0026plusmn;\u0026thinsp;0.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.49\u0026thinsp;\u0026plusmn;\u0026thinsp;0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e\u003cem\u003e0.183\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.006**\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e\u003cem\u003e0.249\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cstrong\u003ePPD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eBaseline\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e2.15\u0026thinsp;\u0026plusmn;\u0026thinsp;0.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e2.14\u0026thinsp;\u0026plusmn;\u0026thinsp;0.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e\u003cem\u003e0.933\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e6 months\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e1.91\u0026thinsp;\u0026plusmn;\u0026thinsp;0.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e1.87\u0026thinsp;\u0026plusmn;\u0026thinsp;0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e\u003cem\u003e0.538\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.001**\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.001**\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cstrong\u003eCAL\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eBaseline\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e4.33\u0026thinsp;\u0026plusmn;\u0026thinsp;0.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e4.55\u0026thinsp;\u0026plusmn;\u0026thinsp;0.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e\u003cem\u003e0.266\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e6 months\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e2.68\u0026thinsp;\u0026plusmn;\u0026thinsp;0.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e2.63\u0026thinsp;\u0026plusmn;\u0026thinsp;0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e\u003cem\u003e0.826\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.001**\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.001**\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cstrong\u003eVRD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eBaseline\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e2.18\u0026thinsp;\u0026plusmn;\u0026thinsp;0.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e2.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e\u003cem\u003e0.155\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e6 months\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e0.75\u0026thinsp;\u0026plusmn;\u0026thinsp;0.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.75\u0026thinsp;\u0026plusmn;\u0026thinsp;0.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e\u003cem\u003e0.996\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.001**\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.001**\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cstrong\u003eHRW\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eBaseline\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e3.8\u0026thinsp;\u0026plusmn;\u0026thinsp;1.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e4\u0026thinsp;\u0026plusmn;\u0026thinsp;1.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e\u003cem\u003e0.493\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e6 months\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e1.8\u0026thinsp;\u0026plusmn;\u0026thinsp;1.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e1.77\u0026thinsp;\u0026plusmn;\u0026thinsp;1.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e\u003cem\u003e0.937\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.001**\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.001**\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cstrong\u003eGT\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eBaseline\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e1.18\u0026thinsp;\u0026plusmn;\u0026thinsp;0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e1.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e\u003cem\u003e0.735\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e6 months\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e1.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e1.59\u0026thinsp;\u0026plusmn;\u0026thinsp;0.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e\u003cem\u003e0.833\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.001**\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.001**\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cstrong\u003eAGW\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eBaseline\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e0.98\u0026thinsp;\u0026plusmn;\u0026thinsp;0.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.80\u0026thinsp;\u0026plusmn;\u0026thinsp;0.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e\u003cem\u003e0.238\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e6 months\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e1.85\u0026thinsp;\u0026plusmn;\u0026thinsp;1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e1.63\u0026thinsp;\u0026plusmn;\u0026thinsp;0.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e\u003cem\u003e0.259\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.001**\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.001**\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cstrong\u003eKGW\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eBaseline\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e3.13\u0026thinsp;\u0026plusmn;\u0026thinsp;0.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e2.93\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e\u003cem\u003e0.229\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e6 months\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e3.76\u0026thinsp;\u0026plusmn;\u0026thinsp;1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e3.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e\u003cem\u003e0.185\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.001**\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.001**\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\"\u003eData are expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation. P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\"\u003ePI: Plaque Index, GI: Gingival Index, PPD: Probing Pocket Depth, CAL: Clinical Attachment Level, VRD: Vertical Recession Depth, HRW: Horizontal Recession Width, GT: Gingival Thickness, AGW: Attached Gingival Width, KGW: Keratinized Gingival Width.\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\"\u003e*There is a significant difference when comparing pre- and post-treatment data (PairedSample Test **\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01, *\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\"\u003e*There is a significant difference when comparing test and control group data ( \u003csup\u003e\u003cem\u003ea\u003c/em\u003e\u003c/sup\u003e\u003cem\u003eIndependent Sample T test)\u003c/em\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003cp\u003eWhen comparing PI values in the test and control groups before treatment and at 6 months after treatment, an increase was observed at 6 months after treatment compared with baseline levels, but the difference was not statistically significant (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05). When GI values were compared, no statistically significant increase was observed in the test group at 6 months post-treatment compared with pre-treatment (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05), whereas this increase was significant in the control group (\u003cem\u003eP\u0026thinsp;=\u0026thinsp;0\u003c/em\u003e.006). When comparing PPD, CAL, VRD, and HRW values in the test and control groups before treatment and 6 months after surgical treatment, a statistically significant decrease was observed at 6 months after treatment compared to baseline levels (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001), while GT, AGW, and KGW showed a statistically significant increase at 6 months post-treatment compared to baseline levels (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001).\u003c/p\u003e\n \u003cp\u003eWhen comparing the clinical parameters of the test and control groups before treatment and 6 months after surgical treatment, no statistically significant differences were found in PI, GI, PPD, CAL, VRD, HRW, GT, AGW, or KGW (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\"\u003e\n \u003ch2\u003eCRC and MRC\u003c/h2\u003e\n \u003cp\u003eThe comparison of root closure rates between the test and control groups is shown in Fig. 4. The mean root closure rates were 70.26\u0026thinsp;\u0026plusmn;\u0026thinsp;29.92% in the test group and 68.78\u0026thinsp;\u0026plusmn;\u0026thinsp;31.36% in the control group. When comparing the groups, no statistically significant difference in mean root closure rates was found (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05). At 6 months, the complete root closure rates were 41.3% in the test group and 42.5% in the control group. No significant difference in complete root closure rates was found between the groups (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\"\u003e\n \u003ch2\u003ePatient-Based Data Evaluation\u003c/h2\u003e\n \u003cp\u003ePain, bleeding, edema presence, and the number of analgesics used during the first 9 days post-surgery were compared inter- and intra-group in Fig. 5. In intra-group evaluations, statistically significant decreases in bleeding, pain, edema, and analgesic use were observed in both groups during the 9 days after surgery (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001). Between-group evaluations showed no statistically significant differences in pain, bleeding, edema, or analgesic use (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e\n \u003cp\u003eWhen the severity of pain and edema perceived by the patients was evaluated, statistically significant reductions were observed in both the control and test groups during the 9 days following surgery (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001) (Table 3). However, when the severity of pain and edema was compared between the groups, no significant differences were found (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05) (Table 3).\u003c/p\u003e\n \u003cdiv\u003e\n \u003cdiv align=\"left\" colname=\"c1\" colnum=\"1\"\u003e\u003cimg 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\"\u003e\u003c/div\u003e\n \u003cdiv align=\"left\" colname=\"c2\" colnum=\"2\"\u003ePatient satisfaction analysis results\u003c/div\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\"\u003e\n \u003cp\u003eWhen the patient satisfaction analysis data were evaluated, statistically significant increases in PCS and PAS scores and a significant decrease in PSS scores were observed in both the test and control groups at 6 months after surgical treatment, compared with baseline (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001). In the intergroup comparison, no statistically significant difference in PAS and PSS scores was observed before or after surgical treatment (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05). On the other hand, although no significant difference was observed in PCS scores at baseline (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05), a statistically significantly greater increase was found in the test group compared to the control group at 6 months postoperative treatment (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001) (Table 4).\u003c/p\u003e\n \u003cdiv\u003e\n \u003cdiv align=\"left\" colname=\"c1\" colnum=\"1\"\u003e\u003cimg 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\"\u003e\u003c/div\u003e\n \u003c/div\u003e\n\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study is the first to evaluate the effect of HA on both clinical improvements and patient comfort and satisfaction in the surgical treatment of gingival recession. The split-mouth study found that when CAF was applied with or without HA in the surgical treatment of gingival recession, positive improvements were observed in clinical parameters (PPD, CAL, VRD, HRW, KGW, AGW, GT) and patient satisfaction analysis (PCS, PAS, and PSS) compared to baseline data. Furthermore, the study showed that while HA had no additional effect on clinical improvements at 6 months in the treatment of GR, it had a significantly positive effect on patient comfort.\u003c/p\u003e \u003cp\u003eIn our study, although no significant differences were observed between the groups in baseline and 6-month VRD, HGR, and CAL values, we analyzed changes in defect size and observed significant improvements in both groups at 6 months post-surgery compared to baseline. The VRD, HRW, and CAL data of the control group in our study are consistent with studies comparing CAF alone to CAF with additional biomaterials in control groups [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. In their study, Badge et al.\u003csup\u003e17\u003c/sup\u003e, divided 45 patients into three groups and treated them with CAF, CAF\u0026thinsp;+\u0026thinsp;HA (0.2% gengigel), and CAF+membrane techniques. At the 6-month follow-up, although no differences were observed between groups in VRD, HGR, CAL, and PPD values, improvement was observed in all groups compared with baseline [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Additionally, Rajan et al.\u003csup\u003e27\u003c/sup\u003e compared CAF\u0026thinsp;+\u0026thinsp;HA (test) with CAF\u0026thinsp;+\u0026thinsp;CTG (control) and reported that at 9 months post-surgery follow-up, there was a significant difference in favor of the test group only in CAL values, while there was no significant difference in VRD, HRW, and PPD compared to the control group. In a study by Kumar et al.\u003csup\u003e11\u003c/sup\u003e, which had a similar design to ours, 0.2% HA (Gengigel) was applied to the test group, and although no significant difference was found between the groups, they reported positive improvements from baseline to 6 months. Similarly, in their 18-month follow-up parallel technique study, in which they applied CAF and CAF\u0026thinsp;+\u0026thinsp;HA in Miller I defects, Pilloni et al.\u003csup\u003e23\u003c/sup\u003e reported a significant difference in VRD and CAL values in favor of the test group at 18 months post-surgery, while there was no difference in PPD. They also showed significant improvements in VRD, PPD, and CAL values in both the test and control groups. Another study, in its 6-month follow-up, reported no significant difference in VRD and CAL levels between groups, but significant improvements in both groups within each group [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Although our study included different control groups, its findings are consistent with those of these studies. Our study demonstrates that CAF reduces the size of gingival recession defects but suggests that HA does not have an additional effect on the healing process.\u003c/p\u003e \u003cp\u003eThe study found that, while there were no significant differences between the groups in GI, when evaluated individually, the control group showed a significant increase at 6 months, whereas no significant differences were observed in the test group. Rajan et al.\u003csup\u003e27\u003c/sup\u003e applied CAF\u0026thinsp;+\u0026thinsp;HA and CAF\u0026thinsp;+\u0026thinsp;CTG to Miller I-II gingival defect areas in their 9-month follow-up study and found no significant differences between groups in GI data, but a significant decrease in both groups within each group. The reason for this difference may be the use of additional grafts in the control group. Based on the GI findings of our study, we can suggest that HA has a supportive role in reducing gingival inflammation.\u003c/p\u003e \u003cp\u003eGingival phenotype affects the long-term stability of soft tissues after surgery and increases the rates of thick gingiva MRC and CRC [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Therefore, although clinicians primarily aim for CRC in GR treatments, they also aim to increase GT and KGW. It has been emphasized that bilaminar techniques involving FGG and CTG provide the best increase in GT and KGW when these are insufficient [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. On the other hand, a randomized controlled study found that CAF applied alone when baseline GT was \u0026gt;\u0026thinsp;0.8 mm yielded results similar to those with CAF combined with CTG [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Indeed, based on this information, our study selected patients with GT\u0026thinsp;\u0026gt;\u0026thinsp;0.8 and evaluated the effectiveness of HA with CAF, aiming to eliminate the discomfort and complications that CTG can cause. In the current study, GT in the test group increased from an initial mean of 1.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.32 mm to 1.59\u0026thinsp;\u0026plusmn;\u0026thinsp;0.34 mm at 6 months post-surgery, while in the control group, it increased from 1.18\u0026thinsp;\u0026plusmn;\u0026thinsp;0.23 mm to 1.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.34 mm. KGW increased from an average of 2.93\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7 mm to 3.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8 mm in the test group and from 3.13\u0026thinsp;\u0026plusmn;\u0026thinsp;0.79 mm to 3.76\u0026thinsp;\u0026plusmn;\u0026thinsp;1.02 mm in the control group. In our study, no significant difference was found between the groups for GT, KGW, and AGW, while significant increases were observed in both groups during the follow-up period. The increase in keratinized tissue after CAF can be explained by the tendency to return to the genetically determined position of the MGJ [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Our findings regarding KGW were parallel to the few existing studies on HA application in GR treatments [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. However, these studies did not adequately evaluate GT [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Gorski et al.\u003csup\u003e32\u003c/sup\u003e, in their study evaluating the HA effect in addition to tunnel\u0026thinsp;+\u0026thinsp;CTG, emphasized that although KGW and GT values increased over the follow-up period, they did not detect any differences between the groups. These data are consistent with our study.\u003c/p\u003e \u003cp\u003eIn the current study, the MRC in the test group was 70.26\u0026thinsp;\u0026plusmn;\u0026thinsp;29.92%, while in the control group it was 68.78\u0026thinsp;\u0026plusmn;\u0026thinsp;31.36%; the difference between the groups was not statistically significant. The MRC in the control group is consistent with the root coverage reported in the control groups of the studies by Silva et al.\u003csup\u003e33\u003c/sup\u003e and Woodyard et al.\u003csup\u003e7\u003c/sup\u003e The average root coverage in the test group in the current study was similar to that reported by Kumar et al.\u003csup\u003e11\u003c/sup\u003e Furthermore, the authors reported CRC values at 6 months as 40% and 20% for the test and control groups, respectively [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], and it was observed that the test group's values were very close to the CRC of the test group in our study (19 out of 46 defects; 41.3%). Furthermore, the CRC in our control group was 42.5% (17 out of 40 defects). However, apart from the CRC of the control group in the current study, the total and MRC scores were also lower than those reported by Pilloni et al.\u003csup\u003e23\u003c/sup\u003e in the study using the parallel technique. In their study, Rajan et al.\u003csup\u003e27\u003c/sup\u003e planned CAF\u0026thinsp;+\u0026thinsp;HA (test) and CAF\u0026thinsp;+\u0026thinsp;CTG (control) in a divided-mouth design and reported CRCs of 77.84% and 82.15%, respectively, in 20 patients. Although the MRC in the test group was similar to that in the current study, the high MRC observed in the control group is thought to be related to CTG [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. However, the percentage of HA (gengigel) used in the study and the complete root coverage scores were not specified [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn our study, patient comfort in the postoperative period was evaluated by monitoring pain, edema, and bleeding scores, as well as the number of painkillers patients reported using over 9 days. According to the study results, 18 out of 20 patients reported feeling pain on the first day after surgery, and 14 of these patients reported needing painkillers. Furthermore, although the most severe pain scores recorded on the first day were slightly higher in the control group than in the test group (4.89\u0026thinsp;\u0026plusmn;\u0026thinsp;2.87, 4.56\u0026thinsp;\u0026plusmn;\u0026thinsp;2.62), this difference was not statistically significant on subsequent days. Although there was no statistically significant difference in pain intensity between the groups on the following days, regular and significant decreases were determined within the group daily. On the second day, 6 patients; on the third day, 5 patients; and on the fourth day, only 3 out of 20 patients reported feeling the need to use painkillers. The amount of analgesic used decreased gradually over the follow-up days but did not differ between the groups. When bleeding parameters were examined, 10 patients in each group reported bleeding on the 1st day; however, leakage-type bleeding in the first days after surgery was considered normal due to the dynamic structure of the mouth. Although bleeding, edema, pain scores, and the amount of pain medication used, which could be used as indicators of patient comfort and return to daily routine in the postoperative period, showed small differences between the groups, these differences were not statistically significant. Only one study in the literature applied HA for the treatment of GR and evaluated the postoperative period [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. In their study, Pilloni et al.\u003csup\u003e23\u003c/sup\u003e divided 30 patients into two groups and applied CAF\u0026thinsp;+\u0026thinsp;HA(test) and CAF alone (control) and evaluated the patients' pain, edema and daily comfort over 7 days and found no difference between the groups in terms of pain parameters, while the test group showed significantly better results in terms of comfort and edema [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. However, unlike the present study, this study did not use a split-mouth design, used a different HA biomaterial (cross-linked HA 16 mg/ml), and the pain felt and edema reported by patients were subjective parameters, making it impossible to compare with our data. Furthermore, in our study, consistent with previous studies [34, 35], HA use was well tolerated and did not cause any immunological complications or toxic effects among patients who completed their treatments. Based on the results of the current study, we believe that the additional use of HA in CAF treatment for GR does not cause greater discomfort in the postoperative period than placebo.\u003c/p\u003e \u003cp\u003eIn the current study, patient satisfaction was analyzed by comparing PCS, PSS, and PAS scores before treatment and at 6 months. When all three parameters were evaluated, significant improvements were reported in 6 months compared to baseline levels within the group. When comparing groups, no significant difference was found between baseline and 6-month data for PAS and PSS, whereas PCS values at 6 months showed significantly better results in the test group than in the control group. In the study by Gorski et al.\u003csup\u003e28\u003c/sup\u003e, which evaluated the effectiveness of HA in addition to tunnel\u0026thinsp;+\u0026thinsp;CTG, an aesthetic evaluation was performed; however, unlike in the current study, it was not patient-oriented. It was performed by an independent periodontist, who evaluated indicators such as soft tissue, marginal tissue contour, root coverage amounts, and color mismatch, and only soft tissue showed positive results in favor of the test group [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].In a case series, 15 patients were treated with modified coronally advanced tunnel (MCAT)\u0026thinsp;+\u0026thinsp;CTG+HA, and a high MRC (85%) was reported [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. An independent observer rated the aesthetic score based on photographs, particularly considering the degree of root coverage, as 7.9 out of 10 [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. The aesthetic score in the current study was patient-reported, with values of 7.95 for the control group and 8.43 for the test group. No significant difference was found between the groups, and the values were similar to those reported by Lanzrein et al.\u003csup\u003e36\u003c/sup\u003e However, due to the absence of a control group in this study, the evaluation was not patient-oriented, and the surgical procedure differed from that in the current study, making it impossible to reach a clear conclusion.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, our study demonstrated that both CAF and the additional application of HA to CAF were successful in covering the root surface in the treatment of multiple RT1. It also determined that additional HA application to CAF may be beneficial for reducing gingival inflammation in periodontal treatments for GR. Moreover, it suggests that the use of HA in addition to CAF may improve patient comfort scores in the GR. However, randomized, well-designed, and histological evaluation studies are needed in the future to better explain the mechanisms of action of HA in GR treatments.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eF\u0026Uuml; and FOD contributed to data acquisition, analysis, and interpretation and drafted the manuscript and also contributed to the conception, design, data analysis, and interpretation and critically revised the manuscript. All authors gave their final approval and agreed to be accountable for all aspects of the work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData were available on request from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval\u003c/strong\u003e The study was approved by the Clinical Research Ethics Committee of Ondokuz Mayıs University, Samsun, Turkey (protocol number 2024/178) and registered in the Turkish Ministry of Health\u0026rsquo;s Turkish Medicines and Medical Devices Agency (Approval Number: 2024-031). All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and national research committee and with the Helsinki declaration of 1975, as revised in 2013 and its later amendments or comparable ethical standards.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eConsent to participate\u003c/strong\u003e The patients were informed about the nature of the study and informed consent was obtained from all individual participants included in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e The authors declare no competing interests.\u003c/p\u003e\n"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eGoldstein M, Nasatzky E, Goultschin J, Boyan BD, Schwartz Z (2002) Coverage of previously carious roots is as predictable a procedure as coverage of intact roots. J Periodontol 73:1419\u0026ndash;1426. https://doi.org/10.1902/jop.2002.73.12.1419\u003c/li\u003e\n\u003cli\u003eCairo F, Nieri M, Pagliaro U (2014) Efficacy of periodontal plastic surgery procedures in the treatment of localized facial gingival recessions: a systematic review. J Clin Periodontol 41:S44\u0026ndash;S62. https://doi.org/10.1111/jcpe.12182\u003c/li\u003e\n\u003cli\u003eZucchelli G, Tavelli L, Ravid\u0026agrave; A, et al. (2018) Influence of tooth location on coronally advanced flap procedures for root coverage. J Periodontol 89:1428\u0026ndash;1441. https://doi.org/10.1002/JPER.18-0201\u003c/li\u003e\n\u003cli\u003eEren G, Atilla G (2014) Platelet-rich fibrin in the treatment of localized gingival recessions: a split-mouth randomized clinical trial. Clin Oral Investig 18:1941\u0026ndash;1948. https://doi.org/10.1007/s00784-013-1170-5\u003c/li\u003e\n\u003cli\u003eCairo F, Cortellini P, Pilloni A, et al. (2016) Clinical efficacy of coronally advanced flap with or without connective tissue graft for the treatment of multiple adjacent gingival recessions in the aesthetic area: a randomized controlled clinical trial. J Clin Periodontol 43:849\u0026ndash;856. https://doi.org/10.1111/jcpe.12590\u003c/li\u003e\n\u003cli\u003eSculean A, Schwarz F, Becker J, Brecx M (2007) The application of an enamel matrix protein derivative (Emdogain\u0026reg;) in regenerative periodontal therapy: a review. Med Princ Pract 16:167\u0026ndash;180. https://doi.org/10.1159/000100386\u003c/li\u003e\n\u003cli\u003eWoodyard JG, Greenwell H, Hill M, et al. (2004) The clinical effect of acellular dermal matrix on gingival thickness and root coverage compared to coronally positioned flap alone. J Periodontol 75:44\u0026ndash;56. https://doi.org/10.1902/jop.2004.75.1.44\u003c/li\u003e\n\u003cli\u003eLorenzo R, Garc\u0026iacute;a V, Orsini M, Martin C, Sanz M (2012) Clinical efficacy of a xenogeneic collagen matrix in augmenting keratinized mucosa around implants: a randomized controlled prospective clinical trial. Clin Oral Implants Res 23:316\u0026ndash;324. https://doi.org/10.1111/j.1600-0501.2011.02260.x\u003c/li\u003e\n\u003cli\u003eBarootchi S, Tavelli L, Vinueza M, et al. (2025) Autologous platelet concentrates in root coverage procedures. Periodontol 2000 97:215\u0026ndash;235. https://doi.org/10.1111/prd.12614\u003c/li\u003e\n\u003cli\u003eBozkurt Doğan Ş, \u0026Ouml;ng\u0026ouml;z Dede F, Ballı U, et al. (2015) Concentrated growth factor in the treatment of adjacent multiple gingival recessions: a split-mouth randomized clinical trial. J Clin Periodontol 42:868\u0026ndash;875. https://doi.org/10.1111/jcpe.12444\u003c/li\u003e\n\u003cli\u003eKumar R, Srinivas M, Pai J, et al. (2014) Efficacy of hyaluronic acid in root coverage procedures as an adjunct to coronally advanced flap in Miller class I recession: a clinical study. J Indian Soc Periodontol 18:746. https://doi.org/10.4103/0972-124X.147411\u003c/li\u003e\n\u003cli\u003eShirakata Y, Nakamura T, Kawakami Y, et al. (2021) Healing of buccal gingival recessions following treatment with coronally advanced flap alone or combined with a cross-linked hyaluronic acid gel. J Clin Periodontol 48:570\u0026ndash;580. https://doi.org/10.1111/jcpe.13433\u003c/li\u003e\n\u003cli\u003eVedamurthy M (2004) Soft tissue augmentation\u0026mdash;use of hyaluronic acid as dermal filler. Indian J Dermatol Venereol Leprol 70:383\u0026ndash;387. PMID:17642675\u003c/li\u003e\n\u003cli\u003eKeen MA (2017) Hyaluronic acid in dermatology. Skinmed 15:441\u0026ndash;448\u003c/li\u003e\n\u003cli\u003eCasale M, Moffa A, Vella P, et al. (2016) Hyaluronic acid: perspectives in dentistry. Int J Immunopathol Pharmacol 29:572\u0026ndash;582. https://doi.org/10.1177/0394632016652906\u003c/li\u003e\n\u003cli\u003eKalimeri E, Roccuzzo A, St\u0026auml;hli A, et al. (2024) Adjunctive use of hyaluronic acid in the treatment of gingival recessions: a systematic review and meta-analysis. Clin Oral Investig 28:329. https://doi.org/10.1007/s00784-024-05701-7\u003c/li\u003e\n\u003cli\u003eBagde H, Pawar SK, Vasisth D, et al. (2023) Comparison of amnion membrane and hyaluronic acid in gingival recession coverage and gain in clinical attachment level following coronally advanced flap procedure\u0026mdash;a clinical study.J Pharm Bioallied Sci 15(Suppl 2):S1104\u0026ndash;S1107.https://doi.org/10.4103/jpbs.jpbs_202_23\u003c/li\u003e\n\u003cli\u003eJepsen S, Caton JG, Albandar JM, et al. (2018) Periodontal manifestations of systemic diseases and developmental and acquired conditions. J Periodontol 89:S237\u0026ndash;S248. https://doi.org/10.1002/JPER.17-0733\u003c/li\u003e\n\u003cli\u003eCairo F, Nieri M, Cincinelli S, Mervelt J, Pagliaro U (2011) The interproximal clinical attachment level to classify gingival recessions and predict root coverage outcomes. J Clin Periodontol 38:661\u0026ndash;666. https://doi.org/10.1111/j.1600-051X.2011.01732.x\u003c/li\u003e\n\u003cli\u003eL\u0026ouml;e H, Silness J (1963) Periodontal disease in pregnancy I. Prevalence and severity. Acta Odontol Scand 21:533\u0026ndash;551. https://doi.org/10.3109/00016356309011240\u003c/li\u003e\n\u003cli\u003eSilness J, L\u0026ouml;e H (1964) Periodontal disease in pregnancy II. Correlation between oral hygiene and periodontal condition. Acta Odontol Scand 22:121\u0026ndash;135. https://doi.org/10.3109/00016356408993968\u003c/li\u003e\n\u003cli\u003eSoltani P, Yaghini J, Rafiei K, et al. (2023) Comparative evaluation of the accuracy of gingival thickness measurement by clinical evaluation and intraoral ultrasonography. J Clin Med 12:4395. https://doi.org/10.3390/jcm12134395\u003c/li\u003e\n\u003cli\u003ePilloni A, Schmidlin PR, Sahrmann P, et al. (2019) Effectiveness of adjunctive hyaluronic acid application in coronally advanced flap in Miller class I single gingival recession sites. Clin Oral Investig 23:1133\u0026ndash;1141. https://doi.org/10.1007/s00784-018-2537-4\u003c/li\u003e\n\u003cli\u003eDe Sanctis M, Zucchelli G (2007) Coronally advanced flap: a modified surgical approach for isolated recession-type defects. J Clin Periodontol 34:262\u0026ndash;268. https://doi.org/10.1111/j.1600-051X.2006.01039.x\u003c/li\u003e\n\u003cli\u003eDel Pizzo M, Zucchelli G, Modica F, Villa R, Debernardi C (2005) Coronally advanced flap with or without enamel matrix derivative for root coverage. J Clin Periodontol 32:1181\u0026ndash;1187. https://doi.org/10.1111/j.1600-051X.2005.00831.x\u003c/li\u003e\n\u003cli\u003eSharma A, Wadhawan A (2022) Comparative evaluation of coronally advanced flap with and without Biomesh\u0026reg; membrane. J Med Life 15:705\u0026ndash;716. https://doi.org/10.25122/jml-2021-0109\u003c/li\u003e\n\u003cli\u003eRajan P, Rao NM, Nera M, Rahaman SM (2015) Hyaluronon as an adjunct to coronally advanced flap. NJIRM 6(2).\u003c/li\u003e\n\u003cli\u003eG\u0026oacute;rski B, Szerszeń M, Kaczyński T (2022) Effect of 24% EDTA root conditioning on the outcome of modified coronally advanced tunnel technique. Clin Oral Investig 26:1761\u0026ndash;1772. https://doi.org/10.1007/s00784-021-04151-9\u003c/li\u003e\n\u003cli\u003eRasperini G, Codari M, Paroni L, et al. (2020) The influence of gingival phenotype on the outcomes of coronally advanced flap. Int J Periodontics Restorative Dent 40:e27\u0026ndash;e34. https://doi.org/10.11607/prd.4272\u003c/li\u003e\n\u003cli\u003eBarootchi S, Tavelli L, Zucchelli G, Giannobile WV, Wang HL (2020) Gingival phenotype modification therapies on natural teeth. J Periodontol 91:1386\u0026ndash;1399. https://doi.org/10.1002/JPER.19-0715\u003c/li\u003e\n\u003cli\u003eAnjcamo A, Bergenholtz A, Hugoson A, Ainamo J (1992) Location of the mucogingival junction 18 years after apically repositioned flap surgery. J Clin Periodontol 19:49\u0026ndash;52. https://doi.org/10.1111/j.1600-051x.1992.tb01148.x\u003c/li\u003e\n\u003cli\u003eG\u0026oacute;rski B, Skierska I, Szerszeń M, Mańka-Malara K (2023) Tunnel technique with cross-linked hyaluronic acid in addition to subepithelial connective tissue graft. Clin Oral Investig 27:2395\u0026ndash;2406. https://doi.org/10.1007/s00784-023-04887-6\u003c/li\u003e\n\u003cli\u003eSilva RC, Joly JC, de Lima AF, Tatakis DN (2004) Root coverage using the coronally positioned flap. J Periodontol 75:413\u0026ndash;419. https://doi.org/10.1902/jop.2004.75.3.413 \u003c/li\u003e\n\u003cli\u003eMamajiwala AS, Sethi KS, Raut CP, et al. (2021) Clinical and radiographic evaluation of 0.8% hyaluronic acid as an adjunct to open flap debridement. Clin Oral Investig 25:5257\u0026ndash;5271. https://doi.org/10.1007/s00784-021-03834-7\u003c/li\u003e\n\u003cli\u003eLanzrein C, Guldener K, Imber JC, et al. (2020) Treatment of multiple adjacent recessions with modified coronally advanced tunnel. Quintessence Int 51:710\u0026ndash;719. https://doi.org/10.3290/j.qi.a44808\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Hyaluronic acid, Coronally positioned flap, Gingival recession, Wound healing, Split mouth design","lastPublishedDoi":"10.21203/rs.3.rs-9114410/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9114410/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eHyaluronic acid (HA) has been suggested as a local chemotherapeutic agent in periodontal treatments due to its role in wound healing and periodontal regeneration. The aim of this study is to evaluate the effect of HA use in addition to the coronally advanced flap (CAF) technique in type 1 gingival recession (RT1) on postoperative morbidity, clinical parameters, and patient satisfaction, analyzed at baseline and 6 months.\u003c/p\u003e\u003ch2\u003eMaterials and Methods\u003c/h2\u003e \u003cp\u003eThis split-mouth study included 20 patients (9 males, 11 females) with a mean age of 42.10\u0026thinsp;\u0026plusmn;\u0026thinsp;9.46 years who were diagnosed with bilateral RT1 gingival recession in the maxilla. The right and left defect areas of the 20 patients were randomly assigned to two groups. The control group received CAF (n\u0026thinsp;=\u0026thinsp;20), and the test group received CAF\u0026thinsp;+\u0026thinsp;HA (n\u0026thinsp;=\u0026thinsp;20). Pre-treatment plaque index (PI), gingival index (GI), probing pocket depth (PPD), clinical attachment level (CAL), vertical recession depth (VRD), horizontal recession width (HRW), gingival thickness (GT), attached gingival width (AGW), and keratinized gingival width (KGW) were recorded. Additionally, a satisfaction analysis was conducted, including patient-based comfort (PCS), aesthetic (PAS), and sensitivity (PSS) scores. During the operation, a 0.8% HA gel was applied to the defect areas in the test group, in addition to CAF, while saline was applied to the control group, also in addition to CAF. Pain, bleeding, and edema scores, as well as the number of painkillers, were recorded during the 9-day postoperative period. Clinical parameter measurements and patient satisfaction analyses were repeated 6 months after surgical treatment.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eWhen intra-group comparisons were evaluated, statistically significant improvements were found in clinical parameters, including PPD, CAL, VRD, HRW, GT, AG, and KGW, and patient satisfaction analysis at 6 months compared to baseline in both groups (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Also, while there was no significant difference in GI in the test group (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05), the control group showed an increase at 6 months compared to baseline (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). In the intergroup comparison, a significantly greater increase in PBC was observed in the test group compared to the control group at 6 months (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01), whereas no significant differences were detected in other clinical parameters or patient satisfaction analysis scores (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eOur study suggests that addictive HA to CAF may improve GI and PCS, thereby increase treatment effectiveness and facilitating patient tolerance.\u003c/p\u003e\u003ch2\u003eClinical relevance:\u003c/h2\u003e \u003cp\u003eThe application of 0.8% HA gel in combination with CAF provides positive results in GI and PCS in patients with RT1 gingival recession compared to CAF alone.\u003c/p\u003e","manuscriptTitle":"Clinical Efficacy and Patient Experience of Adjunctive Hyaluronic Acid-based Gel Application With Coronally Advanced Flap in Type 1 Gingival Recession: A Randomized Controlled Clinical Trial","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-24 13:19:50","doi":"10.21203/rs.3.rs-9114410/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"fe92131b-175a-477a-b7f2-e0821cfa58c5","owner":[],"postedDate":"March 24th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-03-27T06:56:23+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-24 13:19:50","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9114410","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9114410","identity":"rs-9114410","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}