Interview Offer Thresholds and Match Success: Optimizing Signal Strategy in the Integrated Plastic Surgery Match

Interview Offer Thresholds and Match Success: Optimizing Signal Strategy in the Integrated Plastic Surgery Match | 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 Interview Offer Thresholds and Match Success: Optimizing Signal Strategy in the Integrated Plastic Surgery Match Elaine Lin, Joey Liang, Melissa M Tran, Ash Patel This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7951684/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Purpose We analyzed plastic surgery applicants in the Texas Seeking Transparency In Application to Residency (STAR) database (2021-24) to assess how number of interview offers (NIO) relates to match outcomes and how this may affect signaling strategy. Methods Multivariable logistic regression compared NIO with other objective factors such as Step scores, and regression models were fit for NIO vs. match probability. Otolaryngology and dermatology were comparison groups. Results Median NIO was 12 ± 9.5, and NIO significantly predicted matching (p < 0.01) and ~ 90% match was predicted at ~ 27 NIO. Applicants with less than 12 NIO had significantly lower match rates (60.4%) than those with 12–27 NIO (82.9%) or greater than 27 NIO (88.5%) (p < 0.01). In the < 12 NIO group, no predictors distinguished matched from unmatched applicants. Otolaryngology applicants had 90% match chance at 26 NIO and 13 for dermatology. Conclusions Our findings suggest increasing plastic surgery signals to 25–30 may promote thoughtful applications while maintaining strong match outcomes. plastic surgery match signaling strategy interview offers match predictors Figures Figure 1 Introduction United States (US) integrated plastic surgery residency programs are difficult to match into. 1 – 3 In 2024, there were only 213 integrated plastic surgery residency positions for 350 applicants, with a match rate of 74.3% for US MD seniors. 4 While objective factors like Step scores, research productivity, and clerkship grades have been reported as significant predictors of matching and have historically been highly regarded, evaluation of applications is increasingly holistic. 3 ,5–9 10–12 This is particularly true with Step 1 becoming Pass/Fail in January 2022. 13 Away rotation performances, letters of recommendation, and other qualitative factors are increasingly important to program directors. 14 , 15 With an increasing number of applicants annually, plastic surgery remains highly competitive. As a result, recent policy changes surrounding the plastic surgery residency match have become a highly discussed topic, particularly the implementation of program signaling in 2022. 16 In response to the increasing number of applications and applicants annually, program signaling was introduced with the Plastic Surgery Common Application (PSCA) in 2022. 17 Program signaling has been proposed to be mutually beneficial to applicants and residency programs. A limited number of signals may incentivize applicants to only apply to a narrow subset of plastic surgery residencies, as was the original goal when signals were introduced by otolaryngology (ENT) residency programs in 2020. 18 For residency programs, signaling may allow programs to limit applications and better identify applicants that fit with their mission or culture. 19 While these benefits have been proposed, their desired effects have not yet been observed in plastic surgery. In the years following the introduction of program signals (2023 and 2024), plastic surgery applicants submitted more applications than prior years without program signals. 17 These discordances suggest discrepancies in the number of signals currently offered in the residency match and the number of signals applicants may require. Subsequently, the plastic surgery match will be moved back to the Electronic Residency Application Service (ERAS) and applicants will now have 20 signals for the 2025-26 application cycle. 20 To evaluate these discrepancies, this study queried the Texas Seeking Transparency in Application to Residency (STAR) database. The Texas STAR database was created in 2018 by the University of Texas Southwestern Medical Center (Dallas, Texas). 21 This national database is populated by survey data from graduating medical students at participating institutions. As of 2024, there are 155 participating institutions across the US. 21 The Texas STAR database has been established as a valuable tool for students to assess their application across specialties, including plastic surgery, ENT, and dermatology. 10 , 11 , 22 , 23 In this study, we assess the relationship between NIO and match outcomes for the 2021–2024 match cycles. We also analyze whether there is a NIO threshold that may balance residency programs’ interests in limiting applications with applicants’ match outcomes in order to inform future changes to program signaling. Methods Data from the Texas STAR Database were collected on September 7, 2024 and analyzed for integrated plastic surgery applicants (2021–2024) who had reported data for all variables of interest. A univariate logistic regression assessed the impact of NIO on match outcomes (matched or unmatched). A multivariable logistic regression accounted for additional covariates: Step 1/2 scores, AOA/Sigma and Gold Humanism membership, number of research experiences, number of abstracts, posters, and presentations, and number of peer-reviewed publications. To explore non-linear relationships between NIO and match probability, the match rate was calculated for each unique NIO. Logarithmic and polynomial regression lines were fit to determine the best relationship, and the NIO for a 90% match probability was calculated. Multivariable logistic regression analyzed predictors of matching in applicants with below-median NIO. Using the best model’s NIO threshold for a 90% match rate, applicants were grouped into below-median NIO, median-to-threshold, and above-threshold groups. Pairwise Mann-Whitney U tests with Bonferroni correction compared match rates and characteristics between groups. Similar analyses were performed for applicants to ENT and dermatology in the Texas STAR database during the same study period. These additional surgical specialties were chosen due to multiple studies indicating similar level of competitiveness and an increased number of signals the past few years. 24 , 25 All analysis was performed in Python (version 3.12.3, Wilmington, Delaware), with significance set at α < 0.05. 26 IRB approval was not indicated, as this de-identified data is publicly available for medical students. Results In the Texas STAR database, there were a total of 255 applicants to plastic surgery. 248 applicants were included for analysis, of which 182 (73.4%) matched. The median NIO was 12 ± 9.5 and mean NIO was 14.7. Univariate regression (area under the curve [AUC]=0.71) and multivariable regression (AUC=0.71) confirmed increased NIO was the only significant predictor of matching (p<0.01). Both non-weighted (R 2 =0.29) and weighted (R 2 =0.25) logarithmic regression were a poor fit for the data. A quadratic function was the best fit for the relationship between NIO and average percent matched (R 2 = 0.52). The peak of this function occurred at NIO = 26.7 and match percentage = 89.3% ( Figure 1a, Table 1 ). For applicants with less than 12 NIO, no variables were significant predictors of match success on multivariable logistic regression. Applicants with 12-27 NIO (82.9% match) and applicants with greater than 27 NIO (88.5% match) had similar match chances (p=0.49) despite the 12-27 NIO group having significantly lower median number of publications (p=0.03). However, applicants with less than 12 NIO (60.4% match) had significantly lower match chances than both groups (p<0.01) on pairwise comparisons and significantly lower Step 2 score (both p<0.01), Step 1 score (both p<0.01), and number of publications (both p<0.01). The characteristics of all applicants are detailed in Table 2 . For the study period, of applicants who matched, the mean NIO was 16.2 ± 9.3 was significantly higher (p<0.001) than the mean NIO of 10.3 ± 8.2 for unmatched applicants. For ENT, there were 537 applicants in the Texas STAR database. 523 applicants were included for analysis, of which 416 (79.5%) matched. The median NIO was 12 ± 7.7 and mean NIO was 13.8. Univariate regression (AUC=0.74) showed increased NIO was significantly associated with matching (p<0.01). Multivariable logistic regression (AUC=0.74) found that along with increased NIO (p<0.01), increased Step 2 CK score (p<0.01) and number of abstracts, posters & presentations (p<0.01) were significant predictors of match success. A non-weighted logarithmic function was the best fit for the data, with R 2 =0.51. Minimum NIO for 90% chance of matching was calculated to be 26.2 ( Figure 1b, Table 1 ). For applicants with NIO less than 12, only increased Step 2 score (p<0.01) and number of abstracts, posters & presentations (p=0.03) were significantly predictive of matching on multivariable logistic regression. Match chances were similar (p=0.11) between applicants with 12-26 NIO (90.9% matched) and applicants with greater than 26 NIO (82.5% matched) even though 12-26 NIO group had lower Step 1 score (p<0.01) and fewer median number of publications (p<0.01). Like in plastic surgery, ENT applicants with less than 12 NIO had significantly lower match chances (66.7%) than the other two groups on pairwise comparisons (both p<0.01) and significantly lower Step 2 score (both p<0.01), number of research experiences (p=0.02 compared to greater than 26 NIO group and p<0.01 compared to 12-26 NIO group), and number of publications (both p<0.01). For the study period, of applicants who matched, the mean NIO was 14.9 ± 7.4 compared to 9.5 ± 7.3 for unmatched applicants which was significantly different (p<0.001). For dermatology, there were 538 applicants, of which 420 were included for analysis and 350 matched (82.3%). The median NIO was 8 ± 5.3 and mean NIO was 9.3. Univariate regression (AUC=0.69) showed increased NIO was significantly associated with matching (p<0.01). Multivariable logistic regression (AUC=0.72). NIO was found to be significantly associated with matching (p<0.01), and increased Step 1 score was also found to be associated with matching (p=0.02). A non-weighted logarithmic model was the best fit for the data, with R 2 =0.72 and 90% chance of matching calculated at NIO=13.3 ( Figure 1c, Table 1 ). For applicants with NIO less than 8, multivariable logistic regression did not show any significant predictors of matching. Match chance for greater than 13 NIO (97.6% matched) was significantly higher than that of match chance for 8-13 NIO (85.4%), p<0.01. The greater than 13 NIO group had higher number of research experiences (p<0.01) and number of publications (p<0.01). For the study period, of applicants who matched, the mean NIO was 9.9 ± 5.4 compared to 6.5 ± 4.1 for unmatched applicants which was significantly different (p<0.001). Discussion This study analyzed the relationship between NIO and match probability in the plastic surgery residency match using the Texas STAR database. We found that plastic surgery applicants had a maximal chance of matching (~90% match) at NIO = 27, but that there was no significant difference in match probability between the 12-27 NIO cohort and the greater than 27 NIO cohort. Interview offers were predictive of a successful match, consistent with prior Texas STAR database studies. 17,29 This suggests diminishing returns with interview offers past the median number but indicates that the increase in signals for the upcoming cycle may be beneficial. Since the implementation of signaling in plastic surgery in 2022, the number of signals offered has gradually increased from 5 to 20 signals in the upcoming 2025-2026 residency match. 20 When program signals were initially introduced, few signals (five) were used to show genuine interest in select programs. 18 While introducing signals has been shown to help applicants obtain interviews at the selected programs, it has been ineffective at limiting the number of applications per applicant. 16 As Pletcher et al. propose, increasing the number of signals can function as an application cap, where a non-signal suggests disinterest. 18 Historically, in other competitive specialties, increasing the number of signals has indeed resulted in fewer applications. From 2023 to 2024, there was an increase in signals from seven to 25 in ENT and three to three gold and 25 silver in dermatology. Subsequently, there was a 30% reduction in average applications submitted for both specialties according to ERAS. 27 This likely represents a domino effect: programs interpret no signal as disinterest and withhold interviews, discouraging applicants from applying to such programs. Per the Association of Professors of Dermatology application guidance in 2024, “there is a near-zero chance of interview invite at non-signaled programs”. 28 For ENT applicants, we found that a 90% match rate was at NIO=26 and for dermatology, NIO=13. Thus, a high match rate can be achieved if an applicant received an interview invite at most of their sent signals. In other words, if an ENT applicant was highly competitive and received all of the interview invites of their 25 signals, they would have close to 90% match rate. Likewise, if a dermatology applicant received all interview invites of their three gold signals and 40% of their silver signals (total of 13), they would also have close to 90% match rate. Given the relatively similar applicant characteristics and experiences in the plastic surgery match and ENT or dermatology, it is likely that we would see a similar pattern if the number of signals was increased to close to the NIO that results in 90% match rate. While the plastic surgery match has increased the number of signals to 20 in the upcoming 2026 plastic surgery match, our results suggest that a further increase in the number of signals may be helpful. When preference signaling was first implemented in plastic surgery in the 2022-2023 residency match, the limited number of signals (5) meant that applicants without signals still frequently received interview offers from non-signaled programs, with a 13.5% chance of receiving interviews at programs students did not rotate or send signals at. 16 For comparison, a pooled analysis of the 2023 and 2024 residency match showed that applicants’ chances of interviews from non-signaled programs was close to 3%. 17 Applicants in specialties like ENT (25 signals) have similarly reported that 84% of their interview offers came from signaled programs during the 2023-2024 cycle. 30 Other studies in specialties like orthopedic surgery with higher signal limits (30 signals) have also reported that 78.8% of programs exclusively interviewed applicants who signaled their program. 31 As a result, it is important to set the signal limit – and by extension, the application limit – at an equitable level that does not limit individual applicants’ match outcomes. Based on our NIO model, we propose that the optimal number of signals may be 25-30 signals. Much like other high signaling specialties like ENT and orthopedic surgery, this would allow programs to almost exclusively consider applications from signaled applicants while balancing applicant interests in a successful match outcome. Plastic surgery applicants in this study also reported applying to an average of 76.6 programs. This is similar to the number reported by the NRMP which reports a median of 88 applications for both matched and unmatched applicants. 32 This suggests that even applicants who are competitive are applying to nearly all programs. Prior studies using the Texas STAR database have shown that plastic surgery applicants spend an average of $10,845 on residency application expenses. 33 These expenses may exacerbate existing cost barriers for low-income applicants. In addition to the financial burden placed on applicants, these extra applications also introduce challenges for residency programs. Between 2017 and 2021, the total number of applications for each applicant has increased by 45%, with each program receiving an average of 282 applications in 2021. 34,35 The high volume of applications hinders holistic review and obscures factors like program fit. 36 Higher signal limits may promote more thorough, meaningful evaluations which is beneficial for both applicants and programs. In addition to changes to signal limits, several further changes may be considered for future cycles. Much like other specialties like dermatology, who highly value signals, national plastic surgery organizations like ACEPS may consider explicitly advising applicants that the chance of receiving an interview offer for a non-signaled program is near zero. While the anticipated new pricing model for the upcoming 2025-26 cycle incentivizes applicants to limit the number of applications ($220 for the first 20 applications, $745 for up to 45 applications, $1500 for 46 or more applications), enforcing explicit guidelines will further reduce applicant stress and financial strains. On an institutional level, residency programs may consider clarifying their view of preference signaling and whether or not they consider applicants who do not signal their program. In addition, it was notable that for plastic surgery applicants with less than the median number of NIO received, there were no distinguishing objective characteristics between matched and unmatched applicants. This suggests that interview performance or other subjective factors like networking or mentorship connections were important for those applicants who matched with few NIO. Additional resources and guidance on interview preparation will be greatly beneficial for applicants. Over the next application cycles, there may also be a general decrease in the number of interviews most applicants receive after the 20-signal system is incorporated. Kotlier et al. reported that after signals were introduced in the plastic surgery match in the 2022-23 application cycle, applicants received significantly fewer interview offers on average compared to cycles prior and interview offers were more evenly distributed amongst applicants. 37 Similar trends were seen in dermatology and ENT based on NRMP reported survey data between 2023 and 2024 when an increase in signals was implemented. 38 Matched dermatology applicants received a median of 11 interview offers compared to 8 for unmatched in 2023; this was compared to in 2024 where matched applicants received 9 vs unmatched received 7 interview offers. 38 Similarly in ENT, there was a median of 21 interview offers for matched applicants to 14 interview offers for unmatched in 2023, which shifted to 15 vs. 12 between matched and unmatched, respectively in 2024. 38 We can anticipate that the optimal NIO will decrease with the increase in signals in the plastic surgery match as even the most competitive applicants will likely not receive more than 20 interview offers. Applicants in the upcoming plastic surgery match cycles should work with their mentors to strategically craft targeted program lists rather than applying to all programs to reduce financial strain. Limitations Our study has some limitations. The Texas STAR database relies on self-reported data, introducing potential inclusion and reporting bias. We did not perform univariate statistics on objective factors between matched and unmatched applicants as this was not in the scope of this project, though prior studies using this dataset have found such differences. 10,29 While we propose that NIO reflects both objective and subjective aspects of an application, this cannot be confirmed, as factors like away rotation performance remain difficult to quantify. Key match components like networking and mentorship, which may benefit students with fewer NIO, were not addressed. In addition, some programs do not formally extend interview offers to away rotators, meaning that some programs will technically match applicants who did not receive an interview offer; this is anecdotally only applicable to a few programs, but should be kept in mind when interpreting the results. Finally, it is important to acknowledge there are a multitude of reasons for applicants to go unmatched including personal issues which are difficult to assess. Conclusion Our results suggest a threshold of NIO at 27 to be correlated with ~ 90% match for plastic surgery applicants, suggesting interview offers are key in predicting match success compared to other objective factors. Plastic surgery residency applications should consider implementing a limit of 25 to 30 program signals, which may balance residency programs’ desires to limit applications with successful match outcomes for applicants. Surgical specialties with comparable signal limits like ENT demonstrate similar NIO probabilities on match outcomes, suggesting similar trends in application volume and maintained match rate will occur in plastic surgery. Further clarification from national organizations and residency programs on signaling policies and how interview offers will be affected will greatly reduce the financial strains and overall stress plastic surgery applicants face in the match. References Fijany AJ, Zago I, Olsson SE, et al. Recent Trends and Future Directions for the Integrated Plastic Surgery Match. Plast Reconstr Surg Glob Open . 2023;11(6):e5053. doi:10.1097/GOX.0000000000005053 Asserson DB, Sarac BA, Janis JE. A 5-Year Analysis of the Integrated Plastic Surgery Residency Match: The Most Competitive Specialty? J Surg Res . 2022;277:303-309. doi:10.1016/j.jss.2022.04.023 Sarac BA, Janis JE. Matching into Plastic Surgery: Insights into the Data. Plast Reconstr Surg Glob Open . 2022;10(5):e4323. doi:10.1097/GOX.0000000000004323 Charting Outcomes in the Match: Senior Students of U.S. MD Medical Schools, 2024 . National Resident Matching Program; 2024. Accessed December 13, 2024. https://www.nrmp.org/wp-content/uploads/2024/09/Charting_Outcomes_MD_Seniors2024.pdf Mellia JA, Jou C, Rathi S, et al. An In-Depth Analysis of Research Output in Successful Integrated Plastic Surgery Match Applicants and Factors Associated With Matching at Top-Ranked Programs. J Surg Educ . 2021;78(1):282-291. doi:10.1016/j.jsurg.2020.06.026 Ngaage LM, Elegbede A, McGlone KL, et al. Integrated Plastic Surgery Match: Trends in Research Productivity of Successful Candidates. Plast Reconstr Surg . 2020;146(1):193-201. doi:10.1097/PRS.0000000000006928 Keane CA, Akhter MF, Sarac BA, Janis JE. Characteristics of Successful Integrated Plastic Surgery Applicants from US Allopathic Medical Schools without a Home Integrated Program. J Surg Educ . 2022;79(2):551-557. doi:10.1016/j.jsurg.2021.11.002 Wang CY, Mellia JA, Levy L, et al. The Association of a Research Year With Matching Into an Integrated Plastic Surgery Residency. J Surg Res . 2024;303:22-31. doi:10.1016/j.jss.2024.08.010 Elemosho A, Sarac BA, Janis JE. The Law of Diminishing Returns in the Integrated Plastic Surgery Residency Match: A Deeper Look at the Numbers. Plast Reconstr Surg Glob Open . 2024;12(7):e5937. doi:10.1097/GOX.0000000000005937 Hallman TG, Qureshi U, Soltani H, et al. Predictors of Plastic Surgery Applicant Success: An Analysis of the Texas STAR Database. J Craniofac Surg . 2024;35(4):1084. doi:10.1097/SCS.0000000000010153 Ewing JN, Gala Z, Lemdani MS, Crystal D, Broach RB, Azoury SC. Unveiling the Need to Improve Personalized Applicant Tools: A Critical Evaluation of the Reliability of the Texas STAR database in Predicting Match Success for Plastic Surgery Applicants. J Surg Educ . 2024;81(9):1320-1330. doi:10.1016/j.jsurg.2024.06.010 Azoury SC, Kozak GM, Stranix JT, et al. The Independent Plastic Surgery Match (2010-2018): Applicant and Program Trends, Predictors of a Successful Match, and Future Directions. J Surg Educ . 2020;77(1):219-228. doi:10.1016/j.jsurg.2019.07.018 The Step 1 exam is going pass-fail. Now what? AAMC. Accessed December 20, 2024. https://www.aamc.org/news/step-1-exam-going-pass-fail-now-what Lin LO, Makhoul AT, Hackenberger PN, et al. Implications of Pass/Fail Step 1 Scoring: Plastic Surgery Program Director and Applicant Perspective. Plast Reconstr Surg Glob Open . 2020;8(12):e3266. doi:10.1097/GOX.0000000000003266 Powell MS, Rhodes LL, Gonzalez S, Mehta ST. Expected Impact of the Pass/Fail Scoring System for USMLE Step 1 on the Plastic Surgery Residency Selection Process: A National Survey of Plastics Program Directors. Cureus . 14(9):e29411. doi:10.7759/cureus.29411 Sergesketter AR, Song E, Shammas RL, et al. Preference Signaling and the Integrated Plastic Surgery Match: A National Survey Study. J Surg Educ . 2024;81(5):662-670. doi:10.1016/j.jsurg.2024.01.011 Kotlier JL, Mihalic AP, Homsy C. Preference Signals and Away Rotations Greatly Influence Application Success in the Integrated Plastic Surgery Match. J Surg Educ . 2025;82(5):103467. doi:10.1016/j.jsurg.2025.103467 Pletcher SD, Chang CWD, Thorne MC, et al. Interview Invitations for Otolaryngology Residency Positions Across Demographic Groups Following Implementation of Preference Signaling. JAMA Netw Open . 2023;6(3):e231922. doi:10.1001/jamanetworkopen.2023.1922 Tidwell J, Yudien M, Rutledge H, Terhune KP, LaFemina J, Aarons CB. Reshaping Residency Recruitment: Achieving Alignment Between Applicants and Programs in Surgery. J Surg Educ . 2022;79(3):643-654. doi:10.1016/j.jsurg.2022.01.004 ACEPS - Preference Signaling FAQs. Accessed July 17, 2025. https://aceplasticsurgeons.org/apply-plastic-surgery/signaling-FAQs.cgi Texas STAR. Accessed December 20, 2024. https://www.utsouthwestern.edu/education/medical-school/about-the-school/student-affairs/texas-star.html Lenze NR, Mihalic AP, DeMason CE, et al. Predictors of otolaryngology applicant success using the Texas STAR database. Laryngoscope Investig Otolaryngol . 2021;6(2):188-194. doi:10.1002/lio2.549 Stevens CR, Jacob L, Murina AT. Match outcomes of dermatology applicants who pursue research gap year using the Texas STAR database. J Am Acad Dermatol . 2024;91(3):575-576. doi:10.1016/j.jaad.2024.05.054 Singh NP, Boyd CJ. Rapidly Increasing Number and Cost of Residency Applications in Surgery. Am Surg . 2023;89(12):5729-5736. doi:10.1177/00031348231173947 Lefebvre C, Hartman N, Tooze J, Manthey D. Determinants of medical specialty competitiveness. Postgrad Med J . 2020;96(1139):511-514. doi:10.1136/postgradmedj-2019-137160 Python. Published online April 9, 2024. http://www.python.org/ Preliminary Program Signaling Data and Their Impact on Residency Selection . Association of American Medical Colleges; 2023. Accessed July 29, 2025. https://www.aamc.org/services/eras-institutions/program-signaling-data Residency Program Directors Section. Information Regarding the 2024-2025 Application Cycle . Association of Professors of Dermatology; 2024. Accessed July 29, 2025. https://students-residents.aamc.org/media/12386/download Bao E, DePaola N, Mihalic AP, Huston TL. Characterizing the past 5 years of integrated plastic surgery applicants: A Texas STAR analysis. J Plast Reconstr Aesthet Surg . 2025;105:88-94. doi:10.1016/j.bjps.2025.03.054 Yousef A, Nichol A, Watson D. Impact of Applicant Signaling for Otolaryngology Interviews. The Laryngoscope . 2025;135(1):80-86. doi:10.1002/lary.31643 Sorenson JC, Ryan PM, Dennison JG, Ward RA, Fornfeist DS. The Power of Preference Signaling: A Monumental Shift in the Orthopaedic Surgery Application Process. J Am Acad Orthop Surg . 2025;33(2):51-55. doi:10.5435/JAAOS-D-24-00335 Match Data. NRMP. July 17, 2025. Accessed July 31, 2025. https://www.nrmp.org/match-data/ Gordon AM, Ahlering TE. How Does Geographic Region Affect the Total and Individual Costs for Medical Students Applying to the Competitive Surgical Residencies? J Surg Educ . 2022;79(1):147-156. doi:10.1016/j.jsurg.2021.08.016 Singh NP, Kovac S, Boyd CJ, King TW. The Modern Integrated Plastic Surgery Applicant Pays 150% More Than Their Counterparts Four Years Ago. Plast Reconstr Surg Glob Open . 2023;11(12):e5475. doi:10.1097/GOX.0000000000005475 Sarac BA, Janis JE. Matching into Plastic Surgery: Insights into the Data. Plast Reconstr Surg Glob Open . 2022;10(5):e4323. doi:10.1097/GOX.0000000000004323 Molina Burbano F, Yao A, Burish N, et al. Solving Congestion in the Plastic Surgery Match: A Game Theory Analysis. Plast Reconstr Surg . 2019;143(2):634. doi:10.1097/PRS.0000000000005254 Kotlier JL, Mihalic AP, Homsy C. Preference Signals and Away Rotations Greatly Influence Application Success in the Integrated Plastic Surgery Match. J Surg Educ . 2025;82(5):103467. doi:10.1016/j.jsurg.2025.103467 Workbook: Charting Outcomes TM : Applicant Survey Results, Main Residency Match®. Accessed July 30, 2025. https://www.nrmp.org/match-data/2025/07/charting-outcomes-applicant-survey-results-2025-main-residency-match/ Tables Table 1. Minimum NIO for percent chance of matching based on best fit model per specialty. Specialty % Chance of Matching Minimum NIO PRS 50 5.8 60 8.6 70 12.0 80 16.5 90 NA * ENT 50 2.6 60 4.7 70 8.3 80 14.7 90 26.2 DERM 50 2.0 60 3.2 70 5.1 80 8.3 90 13.4 * Quadratic function for PRS did not have a solution for 90% chance of matching as the peak of the function was less than 90% Table 2. Comparison of the median of select applicant characteristics amongst variable number of interview offers by specialty. Specialty PRS ENT DERM NIO 27 26 13 Variable Step 2 Score 250-254 * † 255-259 265-269 250-254 * † 260-264 262-267 255-259* † 260-264 † 260-264 Step 1 Score 240-244 * † 250-254 250-254 240-244 † 245-249 † 255-259 245-249* † 250-254 250-254 # of Research Experiences 6 8 11 6 * † 7 7.5 5* † 6 † 8 # of Peer-Reviewed Publications Published 5 * † 7 † 12 4 * † 6 † 9.5 5.5 * † 8.5 † 12 * Significant difference with second group; † Significant difference with third group; PRS = plastic surgery; ENT = otolaryngology; DERM = dermatology Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 18 Nov, 2025 Reviewers invited by journal 16 Nov, 2025 Editor invited by journal 10 Nov, 2025 Editor assigned by journal 10 Nov, 2025 First submitted to journal 26 Oct, 2025 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. 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Lin","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3UlEQVRIie3OsQrCMBCA4YNAupx2jbToKwScRNFXKRScKnR0cBAEu/QBOvgQQsG5EHCqe8GpS6cOirt6Kjo2HR3yE0gC+bgAmEz/mAWQ0eZyYJl4H8HTEPYhyIF77ckrpCXbEZuxUoVLwK4TX8+YT8C2AtlIehsuVZLTx9xTOsZiDr24biZS0epsiYjFwcELXQvNlJmyLqpzf5GgIvKAmY5IhjRl/SbcwSIDKTREKAwVHgWR+XC0y30UeRU2EjuK0huuJv1B4pdFfZz27cjfN5LvsN8J2zw3mUwmk6YnyFY9bgiySxgAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0001-9078-1186","institution":"Duke University School of Medicine","correspondingAuthor":true,"prefix":"","firstName":"Elaine","middleName":"","lastName":"Lin","suffix":""},{"id":545964642,"identity":"9457f536-b993-4b95-9bc0-41ba2a8c9d88","order_by":1,"name":"Joey Liang","email":"","orcid":"","institution":"Duke University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Joey","middleName":"","lastName":"Liang","suffix":""},{"id":545964643,"identity":"d945a94d-7df0-45f5-9b57-27b401f45c24","order_by":2,"name":"Melissa M Tran","email":"","orcid":"","institution":"Stanford University Department of Surgery","correspondingAuthor":false,"prefix":"","firstName":"Melissa","middleName":"M","lastName":"Tran","suffix":""},{"id":545964644,"identity":"dc90c115-3d40-41a5-a6be-4eb565a69f6d","order_by":3,"name":"Ash Patel","email":"","orcid":"","institution":"Duke University Department of Surgery","correspondingAuthor":false,"prefix":"","firstName":"Ash","middleName":"","lastName":"Patel","suffix":""}],"badges":[],"createdAt":"2025-10-27 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14:12:14","extension":"png","order_by":14,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":67247,"visible":true,"origin":"","legend":"","description":"","filename":"OnlineFig1b.png","url":"https://assets-eu.researchsquare.com/files/rs-7951684/v1/96ef92e0d7086432cefef0fe.png"},{"id":96919403,"identity":"51141076-24f7-496e-a472-3a3382ae9407","added_by":"auto","created_at":"2025-11-27 14:13:49","extension":"png","order_by":15,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":60044,"visible":true,"origin":"","legend":"","description":"","filename":"OnlineFig1c.png","url":"https://assets-eu.researchsquare.com/files/rs-7951684/v1/7906e46cefab25f93ee937c9.png"},{"id":96847431,"identity":"eb06374b-6463-4094-a08b-6e8c22facfeb","added_by":"auto","created_at":"2025-11-26 16:49:58","extension":"xml","order_by":16,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":99457,"visible":true,"origin":"","legend":"","description":"","filename":"GSEDD25001820structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7951684/v1/9a2faa8142aa0764cc1caf3b.xml"},{"id":96919308,"identity":"937b565c-cbb8-4c7a-910c-89f2281daf2c","added_by":"auto","created_at":"2025-11-27 14:13:34","extension":"html","order_by":17,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":104742,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7951684/v1/919f33c0857a9935c65ae65e.html"},{"id":96847416,"identity":"f660c151-8c27-4119-9352-eae1c70bc930","added_by":"auto","created_at":"2025-11-26 16:49:57","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":623639,"visible":true,"origin":"","legend":"\u003cp\u003eQuadratic or logarithmic curves of NIO vs match probability for plastic surgery (a), ENT (b), and dermatology (c) applicants with the 90% match threshold highlighted (or peak if not mathematically computable). For each plot, the curve with the best fit (R\u003csup\u003e2\u003c/sup\u003e) is shown.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7951684/v1/b50284d6a21101f6ca7371cc.png"},{"id":96923189,"identity":"5a4d1707-6ce1-4245-8229-c79736757b7f","added_by":"auto","created_at":"2025-11-27 14:21:06","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1004540,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7951684/v1/15c3c147-9325-48ee-81ff-27cf9d352021.pdf"}],"financialInterests":"","formattedTitle":"Interview Offer Thresholds and Match Success: Optimizing Signal Strategy in the Integrated Plastic Surgery Match","fulltext":[{"header":"Introduction","content":"\u003cp\u003eUnited States (US) integrated plastic surgery residency programs are difficult to match into.\u003csup\u003e\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e In 2024, there were only 213 integrated plastic surgery residency positions for 350 applicants, with a match rate of 74.3% for US MD seniors.\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e While objective factors like Step scores, research productivity, and clerkship grades have been reported as significant predictors of matching and have historically been highly regarded, evaluation of applications is increasingly holistic.\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,5\u0026ndash;9 10\u0026ndash;12\u003c/sup\u003e This is particularly true with Step 1 becoming Pass/Fail in January 2022.\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e Away rotation performances, letters of recommendation, and other qualitative factors are increasingly important to program directors.\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e With an increasing number of applicants annually, plastic surgery remains highly competitive. As a result, recent policy changes surrounding the plastic surgery residency match have become a highly discussed topic, particularly the implementation of program signaling in 2022.\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eIn response to the increasing number of applications and applicants annually, program signaling was introduced with the Plastic Surgery Common Application (PSCA) in 2022.\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e Program signaling has been proposed to be mutually beneficial to applicants and residency programs. A limited number of signals may incentivize applicants to only apply to a narrow subset of plastic surgery residencies, as was the original goal when signals were introduced by otolaryngology (ENT) residency programs in 2020.\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e For residency programs, signaling may allow programs to limit applications and better identify applicants that fit with their mission or culture.\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e While these benefits have been proposed, their desired effects have not yet been observed in plastic surgery. In the years following the introduction of program signals (2023 and 2024), plastic surgery applicants submitted more applications than prior years without program signals.\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e These discordances suggest discrepancies in the number of signals currently offered in the residency match and the number of signals applicants may require. Subsequently, the plastic surgery match will be moved back to the Electronic Residency Application Service (ERAS) and applicants will now have 20 signals for the 2025-26 application cycle.\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eTo evaluate these discrepancies, this study queried the Texas Seeking Transparency in Application to Residency (STAR) database. The Texas STAR database was created in 2018 by the University of Texas Southwestern Medical Center (Dallas, Texas).\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e This national database is populated by survey data from graduating medical students at participating institutions. As of 2024, there are 155 participating institutions across the US.\u003csup\u003e21\u003c/sup\u003e The Texas STAR database has been established as a valuable tool for students to assess their application across specialties, including plastic surgery, ENT, and dermatology.\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e,\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eIn this study, we assess the relationship between NIO and match outcomes for the 2021\u0026ndash;2024 match cycles. We also analyze whether there is a NIO threshold that may balance residency programs\u0026rsquo; interests in limiting applications with applicants\u0026rsquo; match outcomes in order to inform future changes to program signaling.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eData from the Texas STAR Database were collected on September 7, 2024 and analyzed for integrated plastic surgery applicants (2021\u0026ndash;2024) who had reported data for all variables of interest. A univariate logistic regression assessed the impact of NIO on match outcomes (matched or unmatched). A multivariable logistic regression accounted for additional covariates: Step 1/2 scores, AOA/Sigma and Gold Humanism membership, number of research experiences, number of abstracts, posters, and presentations, and number of peer-reviewed publications.\u003c/p\u003e\u003cp\u003eTo explore non-linear relationships between NIO and match probability, the match rate was calculated for each unique NIO. Logarithmic and polynomial regression lines were fit to determine the best relationship, and the NIO for a 90% match probability was calculated. Multivariable logistic regression analyzed predictors of matching in applicants with below-median NIO. Using the best model\u0026rsquo;s NIO threshold for a 90% match rate, applicants were grouped into below-median NIO, median-to-threshold, and above-threshold groups. Pairwise Mann-Whitney U tests with Bonferroni correction compared match rates and characteristics between groups.\u003c/p\u003e\u003cp\u003eSimilar analyses were performed for applicants to ENT and dermatology in the Texas STAR database during the same study period. These additional surgical specialties were chosen due to multiple studies indicating similar level of competitiveness and an increased number of signals the past few years.\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e,\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e All analysis was performed in Python (version 3.12.3, Wilmington, Delaware), with significance set at α\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003csup\u003e26\u003c/sup\u003e IRB approval was not indicated, as this de-identified data is publicly available for medical students.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eIn the Texas STAR database, there were a total of 255 applicants to plastic surgery. 248 applicants were included for analysis, of which 182 (73.4%) matched. The median NIO was 12 \u0026plusmn; 9.5 and mean NIO was 14.7. Univariate regression (area under the curve [AUC]=0.71) and multivariable regression (AUC=0.71) confirmed increased NIO was the only significant predictor of matching (p\u0026lt;0.01). Both non-weighted (R\u003csup\u003e2\u003c/sup\u003e=0.29) and weighted (R\u003csup\u003e2\u003c/sup\u003e=0.25) logarithmic regression were a poor fit for the data. \u0026nbsp;A quadratic function was the best fit for the relationship between NIO and average percent matched (R\u003csup\u003e2\u003c/sup\u003e = 0.52). The peak of this function occurred at NIO = 26.7 and match percentage = 89.3% (\u003cstrong\u003eFigure 1a, Table 1\u003c/strong\u003e). For applicants with less than 12 NIO, no variables were significant predictors of match success on multivariable logistic regression. Applicants with 12-27 NIO (82.9% match) and applicants with greater than 27 NIO (88.5% match) had similar match chances (p=0.49) despite the 12-27 NIO group having significantly lower median number of publications (p=0.03). However, applicants with less than 12 NIO (60.4% match) had significantly lower match chances than both groups (p\u0026lt;0.01) on pairwise comparisons and significantly lower Step 2 score (both p\u0026lt;0.01), Step 1 score (both p\u0026lt;0.01), and number of publications (both p\u0026lt;0.01). The characteristics of all applicants are detailed in \u003cstrong\u003eTable 2\u003c/strong\u003e. For the study period, of applicants who matched, the mean NIO was 16.2 \u0026plusmn; 9.3 was significantly higher (p\u0026lt;0.001) than the mean NIO of 10.3 \u0026plusmn; 8.2 for unmatched applicants.\u003c/p\u003e\n\u003cp skip=\"true\"\u003eFor ENT, there were 537 applicants in the Texas STAR database. 523 applicants were included for analysis, of which 416 (79.5%) matched. The median NIO was 12 \u0026plusmn; 7.7 and mean NIO was 13.8. Univariate regression (AUC=0.74) showed increased NIO was significantly associated with matching (p\u0026lt;0.01). Multivariable logistic regression (AUC=0.74) found that along with increased NIO (p\u0026lt;0.01), increased Step 2 CK score (p\u0026lt;0.01) and number of abstracts, posters \u0026amp; presentations (p\u0026lt;0.01) were significant predictors of match success. A non-weighted logarithmic function was the best fit for the data, with R\u003csup\u003e2\u003c/sup\u003e=0.51. Minimum NIO for 90% chance of matching was calculated to be 26.2 (\u003cstrong\u003eFigure 1b, Table 1\u003c/strong\u003e). For applicants with NIO less than 12, only increased Step 2 score (p\u0026lt;0.01) and number of abstracts, posters \u0026amp; presentations (p=0.03) were significantly predictive of matching on multivariable logistic regression. Match chances were similar (p=0.11) between applicants with 12-26 NIO (90.9% matched) and applicants with greater than 26 NIO (82.5% matched) even though 12-26 NIO group had lower Step 1 score (p\u0026lt;0.01) and fewer median number of publications (p\u0026lt;0.01). Like in plastic surgery, ENT applicants with less than 12 NIO had significantly lower match chances (66.7%) than the other two groups on pairwise comparisons (both p\u0026lt;0.01) and significantly lower Step 2 score (both p\u0026lt;0.01), number of research experiences (p=0.02 compared to greater than 26 NIO group and p\u0026lt;0.01 compared to 12-26 NIO group), and number of publications (both p\u0026lt;0.01). For the study period, of applicants who matched, the mean NIO was 14.9 \u0026plusmn; 7.4 compared to 9.5 \u0026plusmn; 7.3 for unmatched applicants which was significantly different (p\u0026lt;0.001).\u003c/p\u003e\n\u003cp skip=\"true\"\u003eFor dermatology, there were 538 applicants, of which 420 were included for analysis and 350 matched (82.3%). The median NIO was 8 \u0026plusmn; 5.3 and mean NIO was 9.3. Univariate regression (AUC=0.69) showed increased NIO was significantly associated with matching (p\u0026lt;0.01). Multivariable logistic regression (AUC=0.72). NIO was found to be significantly associated with matching (p\u0026lt;0.01), and increased Step 1 score was also found to be associated with matching (p=0.02). A non-weighted logarithmic model was the best fit for the data, with R\u003csup\u003e2\u003c/sup\u003e=0.72 and 90% chance of matching calculated at NIO=13.3 (\u003cstrong\u003eFigure 1c, Table 1\u003c/strong\u003e). For applicants with NIO less than 8, multivariable logistic regression did not show any significant predictors of matching. Match chance for greater than 13 NIO (97.6% matched) was significantly higher than that of match chance for 8-13 NIO (85.4%), p\u0026lt;0.01. The greater than 13 NIO group had higher number of research experiences (p\u0026lt;0.01) and number of publications (p\u0026lt;0.01). For the study period, of applicants who matched, the mean NIO was 9.9 \u0026plusmn; 5.4 compared to 6.5 \u0026plusmn; 4.1 for unmatched applicants which was significantly different (p\u0026lt;0.001).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study analyzed the relationship between NIO and match probability in the plastic surgery residency match using the Texas STAR database. We found that plastic surgery applicants had a maximal chance of matching (~90% match) at NIO = 27, but that there was no significant difference in match probability between the 12-27 NIO cohort and the greater than 27 NIO cohort. Interview offers were predictive of a successful match, consistent with prior Texas STAR database studies.\u003csup\u003e17,29\u003c/sup\u003e This suggests diminishing returns with interview offers past the median number but indicates that the increase in signals for the upcoming cycle may be beneficial. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSince the implementation of signaling in plastic surgery in 2022, the number of signals offered has gradually increased from 5 to 20 signals in the upcoming 2025-2026 residency match.\u003csup\u003e20\u003c/sup\u003e When program signals were initially introduced, few signals (five) were used to show genuine interest in select programs.\u003csup\u003e18\u003c/sup\u003e While introducing signals has been shown to help applicants obtain interviews at the selected programs, it has been ineffective at limiting the number of applications per applicant.\u003csup\u003e16\u003c/sup\u003e As Pletcher et al. propose, increasing the number of signals can function as an application cap, where a non-signal suggests disinterest.\u003csup\u003e18\u003c/sup\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHistorically, in other competitive specialties, increasing the number of signals has indeed resulted in fewer applications. From 2023 to 2024, there was an increase in signals from seven to 25 in ENT and three to three gold and 25 silver in dermatology. Subsequently, there was a 30% reduction in average applications submitted for both specialties according to ERAS.\u003csup\u003e27\u003c/sup\u003e This likely represents a domino effect: programs interpret no signal as disinterest and withhold interviews, discouraging applicants from applying to such programs. Per the Association of Professors of Dermatology application guidance in 2024, \u0026ldquo;there is a near-zero chance of interview invite at non-signaled programs\u0026rdquo;.\u003csup\u003e28\u003c/sup\u003e For ENT applicants, we found that a 90% match rate was at NIO=26 and for dermatology, NIO=13. Thus, a high match rate can be achieved if an applicant received an interview invite at most of their sent signals. In other words, if an ENT applicant was highly competitive and received all of the interview invites of their 25 signals, they would have close to 90% match rate. Likewise, if a dermatology applicant received all interview invites of their three gold signals and 40% of their silver signals (total of 13), they would also have close to 90% match rate. Given the relatively similar applicant characteristics and experiences in the plastic surgery match and ENT or dermatology, it is likely that we would see a similar pattern if the number of signals was increased to close to the NIO that results in 90% match rate.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWhile the plastic surgery match has increased the number of signals to 20 in the upcoming 2026 plastic surgery match, our results suggest that a further increase in the number of signals may be helpful. When preference signaling was first implemented in plastic surgery in the 2022-2023 residency match, the limited number of signals (5) meant that applicants without signals still frequently received interview offers from non-signaled programs, with a 13.5% chance of receiving interviews at programs students did not rotate or send signals at.\u003csup\u003e16\u003c/sup\u003e For comparison, a pooled analysis of the 2023 and 2024 residency match showed that applicants\u0026rsquo; chances of interviews from non-signaled programs was close to 3%.\u003csup\u003e17\u003c/sup\u003e Applicants in specialties like ENT (25 signals) have similarly reported that 84% of their interview offers came from signaled programs during the 2023-2024 cycle.\u003csup\u003e30\u003c/sup\u003e Other studies in specialties like orthopedic surgery with higher signal limits (30 signals) have also reported that 78.8% of programs exclusively interviewed applicants who signaled their program.\u003csup\u003e31\u003c/sup\u003e As a result, it is important to set the signal limit \u0026ndash; and by extension, the application limit \u0026ndash; at an equitable level that does not limit individual applicants\u0026rsquo; match outcomes. Based on our NIO model, we propose that the optimal number of signals may be 25-30 signals. Much like other high signaling specialties like ENT and orthopedic surgery, this would allow programs to almost exclusively consider applications from signaled applicants while balancing applicant interests in a successful match outcome.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePlastic surgery applicants in this study also reported applying to an average of 76.6 programs. This is similar to the number reported by the NRMP which reports a median of 88 applications for both matched and unmatched applicants.\u003csup\u003e32\u003c/sup\u003e This suggests that even applicants who are competitive are applying to nearly all programs. Prior studies using the Texas STAR database have shown that plastic surgery applicants spend an average of $10,845 on residency application expenses.\u003csup\u003e33\u003c/sup\u003e These expenses may exacerbate existing cost barriers for low-income applicants. In addition to the financial burden placed on applicants, these extra applications also introduce challenges for residency programs. Between 2017 and 2021, the total number of applications for each applicant has increased by 45%, with each program receiving an average of 282 applications in 2021.\u003csup\u003e34,35\u003c/sup\u003e The high volume of applications hinders holistic review and obscures factors like program fit.\u003csup\u003e36\u003c/sup\u003e Higher signal limits may promote more thorough, meaningful evaluations which is beneficial for both applicants and programs.\u003c/p\u003e\n\u003cp\u003eIn addition to changes to signal limits, several further changes may be considered for future cycles. Much like other specialties like dermatology, who highly value signals, national plastic surgery organizations like ACEPS may consider explicitly advising applicants that the chance of receiving an interview offer for a non-signaled program is near zero. While the anticipated new pricing model for the upcoming 2025-26 cycle incentivizes applicants to limit the number of applications ($220 for the first 20 applications, $745 for up to 45 applications, $1500 for 46 or more applications), enforcing explicit guidelines will further reduce applicant stress and financial strains. On an institutional level, residency programs may consider clarifying their view of preference signaling and whether or not they consider applicants who do not signal their program. In addition, it was notable that for plastic surgery applicants with less than the median number of NIO received, there were no distinguishing objective characteristics between matched and unmatched applicants. This suggests that interview performance or other subjective factors like networking or mentorship connections were important for those applicants who matched with few NIO. Additional resources and guidance on interview preparation will be greatly beneficial for applicants.\u003c/p\u003e\n\u003cp\u003eOver the next application cycles, there may also be a general decrease in the number of interviews most applicants receive after the 20-signal system is incorporated. Kotlier et al. reported that after signals were introduced in the plastic surgery match in the 2022-23 application cycle, applicants received significantly fewer interview offers on average compared to cycles prior and interview offers were more evenly distributed amongst applicants.\u003csup\u003e37\u003c/sup\u003e Similar trends were seen in dermatology and ENT based on NRMP reported survey data between 2023 and 2024 when an increase in signals was implemented.\u003csup\u003e38\u003c/sup\u003e Matched dermatology applicants received a median of 11 interview offers compared to 8 for unmatched in 2023; this was compared to in 2024 where matched applicants received 9 vs unmatched received 7 interview offers.\u003csup\u003e38\u003c/sup\u003e Similarly in ENT, there was a median of 21 interview offers for matched applicants to 14 interview offers for unmatched in 2023, which shifted to 15 vs. 12 between matched and unmatched, respectively in 2024.\u003csup\u003e38\u003c/sup\u003e We can anticipate that the optimal NIO will decrease with the increase in signals in the plastic surgery match as even the most competitive applicants will likely not receive more than 20 interview offers. Applicants in the upcoming plastic surgery match cycles should work with their mentors to strategically craft targeted program lists rather than applying to all programs to reduce financial strain.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLimitations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOur study has some limitations. The Texas STAR database relies on self-reported data, introducing potential inclusion and reporting bias. We did not perform univariate statistics on objective factors between matched and unmatched applicants as this was not in the scope of this project, though prior studies using this dataset have found such differences.\u003csup\u003e10,29\u003c/sup\u003e While we propose that NIO reflects both objective and subjective aspects of an application, this cannot be confirmed, as factors like away rotation performance remain difficult to quantify. Key match components like networking and mentorship, which may benefit students with fewer NIO, were not addressed. In addition, some programs do not formally extend interview offers to away rotators, meaning that some programs will technically match applicants who did not receive an interview offer; this is anecdotally only applicable to a few programs, but should be kept in mind when interpreting the results. Finally, it is important to acknowledge there are a multitude of reasons for applicants to go unmatched including personal issues which are difficult to assess.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOur results suggest a threshold of NIO at 27 to be correlated with ~\u0026thinsp;90% match for plastic surgery applicants, suggesting interview offers are key in predicting match success compared to other objective factors. Plastic surgery residency applications should consider implementing a limit of 25 to 30 program signals, which may balance residency programs\u0026rsquo; desires to limit applications with successful match outcomes for applicants. Surgical specialties with comparable signal limits like ENT demonstrate similar NIO probabilities on match outcomes, suggesting similar trends in application volume and maintained match rate will occur in plastic surgery. Further clarification from national organizations and residency programs on signaling policies and how interview offers will be affected will greatly reduce the financial strains and overall stress plastic surgery applicants face in the match.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eFijany AJ, Zago I, Olsson SE, et al. Recent Trends and Future Directions for the Integrated Plastic Surgery Match. \u003cem\u003ePlast Reconstr Surg Glob Open\u003c/em\u003e. 2023;11(6):e5053. doi:10.1097/GOX.0000000000005053\u003c/li\u003e\n\u003cli\u003eAsserson DB, Sarac BA, Janis JE. A 5-Year Analysis of the Integrated Plastic Surgery Residency Match: The Most Competitive Specialty? \u003cem\u003eJ Surg Res\u003c/em\u003e. 2022;277:303-309. doi:10.1016/j.jss.2022.04.023\u003c/li\u003e\n\u003cli\u003eSarac BA, Janis JE. Matching into Plastic Surgery: Insights into the Data. \u003cem\u003ePlast Reconstr Surg Glob Open\u003c/em\u003e. 2022;10(5):e4323. doi:10.1097/GOX.0000000000004323\u003c/li\u003e\n\u003cli\u003e\u003cem\u003eCharting Outcomes in the Match: Senior Students of U.S. MD Medical Schools, 2024\u003c/em\u003e. National Resident Matching Program; 2024. Accessed December 13, 2024. https://www.nrmp.org/wp-content/uploads/2024/09/Charting_Outcomes_MD_Seniors2024.pdf\u003c/li\u003e\n\u003cli\u003eMellia JA, Jou C, Rathi S, et al. An In-Depth Analysis of Research Output in Successful Integrated Plastic Surgery Match Applicants and Factors Associated With Matching at Top-Ranked Programs. \u003cem\u003eJ Surg Educ\u003c/em\u003e. 2021;78(1):282-291. doi:10.1016/j.jsurg.2020.06.026\u003c/li\u003e\n\u003cli\u003eNgaage LM, Elegbede A, McGlone KL, et al. Integrated Plastic Surgery Match: Trends in Research Productivity of Successful Candidates. \u003cem\u003ePlast Reconstr Surg\u003c/em\u003e. 2020;146(1):193-201. doi:10.1097/PRS.0000000000006928\u003c/li\u003e\n\u003cli\u003eKeane CA, Akhter MF, Sarac BA, Janis JE. Characteristics of Successful Integrated Plastic Surgery Applicants from US Allopathic Medical Schools without a Home Integrated Program. \u003cem\u003eJ Surg Educ\u003c/em\u003e. 2022;79(2):551-557. doi:10.1016/j.jsurg.2021.11.002\u003c/li\u003e\n\u003cli\u003eWang CY, Mellia JA, Levy L, et al. The Association of a Research Year With Matching Into an Integrated Plastic Surgery Residency. \u003cem\u003eJ Surg Res\u003c/em\u003e. 2024;303:22-31. doi:10.1016/j.jss.2024.08.010\u003c/li\u003e\n\u003cli\u003eElemosho A, Sarac BA, Janis JE. The Law of Diminishing Returns in the Integrated Plastic Surgery Residency Match: A Deeper Look at the Numbers. \u003cem\u003ePlast Reconstr Surg Glob Open\u003c/em\u003e. 2024;12(7):e5937. doi:10.1097/GOX.0000000000005937\u003c/li\u003e\n\u003cli\u003eHallman TG, Qureshi U, Soltani H, et al. Predictors of Plastic Surgery Applicant Success: An Analysis of the Texas STAR Database. \u003cem\u003eJ Craniofac Surg\u003c/em\u003e. 2024;35(4):1084. doi:10.1097/SCS.0000000000010153\u003c/li\u003e\n\u003cli\u003eEwing JN, Gala Z, Lemdani MS, Crystal D, Broach RB, Azoury SC. Unveiling the Need to Improve Personalized Applicant Tools: A Critical Evaluation of the Reliability of the Texas STAR database in Predicting Match Success for Plastic Surgery Applicants. \u003cem\u003eJ Surg Educ\u003c/em\u003e. 2024;81(9):1320-1330. doi:10.1016/j.jsurg.2024.06.010\u003c/li\u003e\n\u003cli\u003eAzoury SC, Kozak GM, Stranix JT, et al. The Independent Plastic Surgery Match (2010-2018): Applicant and Program Trends, Predictors of a Successful Match, and Future Directions. \u003cem\u003eJ Surg Educ\u003c/em\u003e. 2020;77(1):219-228. doi:10.1016/j.jsurg.2019.07.018\u003c/li\u003e\n\u003cli\u003eThe Step 1 exam is going pass-fail. Now what? AAMC. Accessed December 20, 2024. https://www.aamc.org/news/step-1-exam-going-pass-fail-now-what\u003c/li\u003e\n\u003cli\u003eLin LO, Makhoul AT, Hackenberger PN, et al. Implications of Pass/Fail Step 1 Scoring: Plastic Surgery Program Director and Applicant Perspective. \u003cem\u003ePlast Reconstr Surg Glob Open\u003c/em\u003e. 2020;8(12):e3266. doi:10.1097/GOX.0000000000003266\u003c/li\u003e\n\u003cli\u003ePowell MS, Rhodes LL, Gonzalez S, Mehta ST. Expected Impact of the Pass/Fail Scoring System for USMLE Step 1 on the Plastic Surgery Residency Selection Process: A National Survey of Plastics Program Directors. \u003cem\u003eCureus\u003c/em\u003e. 14(9):e29411. doi:10.7759/cureus.29411\u003c/li\u003e\n\u003cli\u003eSergesketter AR, Song E, Shammas RL, et al. Preference Signaling and the Integrated Plastic Surgery Match: A National Survey Study. \u003cem\u003eJ Surg Educ\u003c/em\u003e. 2024;81(5):662-670. doi:10.1016/j.jsurg.2024.01.011\u003c/li\u003e\n\u003cli\u003eKotlier JL, Mihalic AP, Homsy C. Preference Signals and Away Rotations Greatly Influence Application Success in the Integrated Plastic Surgery Match. \u003cem\u003eJ Surg Educ\u003c/em\u003e. 2025;82(5):103467. doi:10.1016/j.jsurg.2025.103467\u003c/li\u003e\n\u003cli\u003ePletcher SD, Chang CWD, Thorne MC, et al. Interview Invitations for Otolaryngology Residency Positions Across Demographic Groups Following Implementation of Preference Signaling. \u003cem\u003eJAMA Netw Open\u003c/em\u003e. 2023;6(3):e231922. doi:10.1001/jamanetworkopen.2023.1922\u003c/li\u003e\n\u003cli\u003eTidwell J, Yudien M, Rutledge H, Terhune KP, LaFemina J, Aarons CB. Reshaping Residency Recruitment: Achieving Alignment Between Applicants and Programs in Surgery. \u003cem\u003eJ Surg Educ\u003c/em\u003e. 2022;79(3):643-654. doi:10.1016/j.jsurg.2022.01.004\u003c/li\u003e\n\u003cli\u003eACEPS - Preference Signaling FAQs. Accessed July 17, 2025. https://aceplasticsurgeons.org/apply-plastic-surgery/signaling-FAQs.cgi\u003c/li\u003e\n\u003cli\u003eTexas STAR. Accessed December 20, 2024. https://www.utsouthwestern.edu/education/medical-school/about-the-school/student-affairs/texas-star.html\u003c/li\u003e\n\u003cli\u003eLenze NR, Mihalic AP, DeMason CE, et al. Predictors of otolaryngology applicant success using the Texas STAR database. \u003cem\u003eLaryngoscope Investig Otolaryngol\u003c/em\u003e. 2021;6(2):188-194. doi:10.1002/lio2.549\u003c/li\u003e\n\u003cli\u003eStevens CR, Jacob L, Murina AT. Match outcomes of dermatology applicants who pursue research gap year using the Texas STAR database. \u003cem\u003eJ Am Acad Dermatol\u003c/em\u003e. 2024;91(3):575-576. doi:10.1016/j.jaad.2024.05.054\u003c/li\u003e\n\u003cli\u003eSingh NP, Boyd CJ. Rapidly Increasing Number and Cost of Residency Applications in Surgery. \u003cem\u003eAm Surg\u003c/em\u003e. 2023;89(12):5729-5736. doi:10.1177/00031348231173947\u003c/li\u003e\n\u003cli\u003eLefebvre C, Hartman N, Tooze J, Manthey D. Determinants of medical specialty competitiveness. \u003cem\u003ePostgrad Med J\u003c/em\u003e. 2020;96(1139):511-514. doi:10.1136/postgradmedj-2019-137160\u003c/li\u003e\n\u003cli\u003ePython. Published online April 9, 2024. http://www.python.org/\u003c/li\u003e\n\u003cli\u003e\u003cem\u003ePreliminary Program Signaling Data and Their Impact on Residency Selection\u003c/em\u003e. Association of American Medical Colleges; 2023. Accessed July 29, 2025. https://www.aamc.org/services/eras-institutions/program-signaling-data\u003c/li\u003e\n\u003cli\u003eResidency Program Directors Section. \u003cem\u003eInformation Regarding the 2024-2025 Application Cycle\u003c/em\u003e. Association of Professors of Dermatology; 2024. Accessed July 29, 2025. https://students-residents.aamc.org/media/12386/download\u003c/li\u003e\n\u003cli\u003eBao E, DePaola N, Mihalic AP, Huston TL. Characterizing the past 5 years of integrated plastic surgery applicants: A Texas STAR analysis. \u003cem\u003eJ Plast Reconstr Aesthet Surg\u003c/em\u003e. 2025;105:88-94. doi:10.1016/j.bjps.2025.03.054\u003c/li\u003e\n\u003cli\u003eYousef A, Nichol A, Watson D. Impact of Applicant Signaling for Otolaryngology Interviews. \u003cem\u003eThe Laryngoscope\u003c/em\u003e. 2025;135(1):80-86. doi:10.1002/lary.31643\u003c/li\u003e\n\u003cli\u003eSorenson JC, Ryan PM, Dennison JG, Ward RA, Fornfeist DS. The Power of Preference Signaling: A Monumental Shift in the Orthopaedic Surgery Application Process. \u003cem\u003eJ Am Acad Orthop Surg\u003c/em\u003e. 2025;33(2):51-55. doi:10.5435/JAAOS-D-24-00335\u003c/li\u003e\n\u003cli\u003eMatch Data. NRMP. July 17, 2025. Accessed July 31, 2025. https://www.nrmp.org/match-data/\u003c/li\u003e\n\u003cli\u003eGordon AM, Ahlering TE. How Does Geographic Region Affect the Total and Individual Costs for Medical Students Applying to the Competitive Surgical Residencies? \u003cem\u003eJ Surg Educ\u003c/em\u003e. 2022;79(1):147-156. doi:10.1016/j.jsurg.2021.08.016\u003c/li\u003e\n\u003cli\u003eSingh NP, Kovac S, Boyd CJ, King TW. The Modern Integrated Plastic Surgery Applicant Pays 150% More Than Their Counterparts Four Years Ago. \u003cem\u003ePlast Reconstr Surg Glob Open\u003c/em\u003e. 2023;11(12):e5475. doi:10.1097/GOX.0000000000005475\u003c/li\u003e\n\u003cli\u003eSarac BA, Janis JE. Matching into Plastic Surgery: Insights into the Data. \u003cem\u003ePlast Reconstr Surg Glob Open\u003c/em\u003e. 2022;10(5):e4323. doi:10.1097/GOX.0000000000004323\u003c/li\u003e\n\u003cli\u003eMolina Burbano F, Yao A, Burish N, et al. Solving Congestion in the Plastic Surgery Match: A Game Theory Analysis. \u003cem\u003ePlast Reconstr Surg\u003c/em\u003e. 2019;143(2):634. doi:10.1097/PRS.0000000000005254\u003c/li\u003e\n\u003cli\u003eKotlier JL, Mihalic AP, Homsy C. Preference Signals and Away Rotations Greatly Influence Application Success in the Integrated Plastic Surgery Match. \u003cem\u003eJ Surg Educ\u003c/em\u003e. 2025;82(5):103467. doi:10.1016/j.jsurg.2025.103467\u003c/li\u003e\n\u003cli\u003eWorkbook: Charting Outcomes\u003csup\u003eTM\u003c/sup\u003e: Applicant Survey Results, Main Residency Match\u0026reg;. Accessed July 30, 2025. https://www.nrmp.org/match-data/2025/07/charting-outcomes-applicant-survey-results-2025-main-residency-match/\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1.\u003c/strong\u003e Minimum NIO for percent chance of matching based on best fit model per specialty.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"408\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSpecialty\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 175px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e% Chance of Matching\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMinimum NIO\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"5\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePRS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 175px;\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 156px;\"\u003e\n \u003cp\u003e5.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 175px;\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 156px;\"\u003e\n \u003cp\u003e8.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 175px;\"\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 156px;\"\u003e\n \u003cp\u003e12.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 175px;\"\u003e\n \u003cp\u003e80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 156px;\"\u003e\n \u003cp\u003e16.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 175px;\"\u003e\n \u003cp\u003e90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 156px;\"\u003e\n \u003cp\u003eNA\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"5\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eENT\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 175px;\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 156px;\"\u003e\n \u003cp\u003e2.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 175px;\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 156px;\"\u003e\n \u003cp\u003e4.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 175px;\"\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 156px;\"\u003e\n \u003cp\u003e8.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 175px;\"\u003e\n \u003cp\u003e80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 156px;\"\u003e\n \u003cp\u003e14.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 175px;\"\u003e\n \u003cp\u003e90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 156px;\"\u003e\n \u003cp\u003e26.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"5\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDERM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 175px;\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 156px;\"\u003e\n \u003cp\u003e2.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 175px;\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 156px;\"\u003e\n \u003cp\u003e3.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 175px;\"\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 156px;\"\u003e\n \u003cp\u003e5.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 175px;\"\u003e\n \u003cp\u003e80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 156px;\"\u003e\n \u003cp\u003e8.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 175px;\"\u003e\n \u003cp\u003e90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 156px;\"\u003e\n \u003cp\u003e13.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003csup\u003e*\u003c/sup\u003eQuadratic function for PRS did not have a solution for 90% chance of matching as the peak of the function was less than 90%\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2.\u003c/strong\u003e Comparison of the median of select applicant characteristics amongst variable number of interview offers by specialty.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"618\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSpecialty\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePRS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eENT\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 186px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDERM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNIO\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;12\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e12-27\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026gt;27\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;12\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e12-26\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026gt;26\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;8\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e8-13\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026gt;13\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003eStep 2 Score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003e250-254\u003csup\u003e*\u003c/sup\u003e\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e255-259\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e265-269\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e250-254\u003csup\u003e*\u003c/sup\u003e\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e260-264\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e262-267\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e255-259*\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e260-264\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e260-264\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003eStep 1 Score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003e240-244\u003csup\u003e*\u003c/sup\u003e\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e250-254\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e250-254\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e240-244\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e245-249\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e255-259\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e245-249*\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e250-254\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e250-254\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e# of Research Experiences\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e6\u003csup\u003e*\u003c/sup\u003e\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e7.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e5*\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e6\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e# of Peer-Reviewed Publications Published\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003e5\u003csup\u003e*\u003c/sup\u003e\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e7\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e4\u003csup\u003e*\u003c/sup\u003e\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e6\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e9.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e5.5\u003csup\u003e*\u003c/sup\u003e\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e8.5\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e* Significant difference with second group; \u003csup\u003e\u0026dagger;\u003c/sup\u003e Significant difference with third group; PRS = plastic surgery; ENT = otolaryngology; DERM = dermatology\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"global-surgical-education-journal-of-the-association-for-surgical-education","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"GSED","sideBox":"Learn more about [Global Surgical Education - Journal of the Association for Surgical Education](https://link.springer.com/journal/44186)","snPcode":"44186","submissionUrl":"https://www.editorialmanager.com/gsed/default1.aspx","title":"Global Surgical Education - Journal of the Association for Surgical Education","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"plastic surgery match, signaling strategy, interview offers, match predictors","lastPublishedDoi":"10.21203/rs.3.rs-7951684/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7951684/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003ePurpose\u003c/h2\u003e\u003cp\u003eWe analyzed plastic surgery applicants in the Texas Seeking Transparency In Application to Residency (STAR) database (2021-24) to assess how number of interview offers (NIO) relates to match outcomes and how this may affect signaling strategy.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eMultivariable logistic regression compared NIO with other objective factors such as Step scores, and regression models were fit for NIO vs. match probability. Otolaryngology and dermatology were comparison groups.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eMedian NIO was 12\u0026thinsp;\u0026plusmn;\u0026thinsp;9.5, and NIO significantly predicted matching (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and ~\u0026thinsp;90% match was predicted at ~\u0026thinsp;27 NIO. Applicants with less than 12 NIO had significantly lower match rates (60.4%) than those with 12\u0026ndash;27 NIO (82.9%) or greater than 27 NIO (88.5%) (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). In the \u0026lt;\u0026thinsp;12 NIO group, no predictors distinguished matched from unmatched applicants. Otolaryngology applicants had 90% match chance at 26 NIO and 13 for dermatology.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eOur findings suggest increasing plastic surgery signals to 25\u0026ndash;30 may promote thoughtful applications while maintaining strong match outcomes.\u003c/p\u003e","manuscriptTitle":"Interview Offer Thresholds and Match Success: Optimizing Signal Strategy in the Integrated Plastic Surgery Match","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-26 16:49:53","doi":"10.21203/rs.3.rs-7951684/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2025-11-19T00:17:52+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-11-17T02:38:34+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"Global Surgical Education - Journal of the Association for Surgical Education","date":"2025-11-11T04:42:58+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-11-10T14:11:37+00:00","index":"","fulltext":""},{"type":"submitted","content":"Global Surgical Education - Journal of the Association for Surgical Education","date":"2025-10-26T14:55:00+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"global-surgical-education-journal-of-the-association-for-surgical-education","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"GSED","sideBox":"Learn more about [Global Surgical Education - Journal of the Association for Surgical Education](https://link.springer.com/journal/44186)","snPcode":"44186","submissionUrl":"https://www.editorialmanager.com/gsed/default1.aspx","title":"Global Surgical Education - Journal of the Association for Surgical Education","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"0787f6ac-2810-4e20-afe8-7dcd489bebf7","owner":[],"postedDate":"November 26th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-02-15T20:04:50+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-26 16:49:53","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7951684","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7951684","identity":"rs-7951684","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}