{"paper_id":"940e2baf-7581-4eaa-ba43-7d885a09b08b","body_text":"Original Paper \nFederica Bologna1, MS; Rosamond Thalken1, MS; Kristen Pepin2, MD, MPH; Matthew \nWilkens1, PhD \n1Department of Information Science, Cornell University \n2Department of Obstetrics and Gynecology, Weill Cornell Medical College \nEndometriosis Online Communities: A Quantitative Analysis  \nAbstract \nBackground: Endometriosis is a chronic condition that affects 10% of people with a \nuterus. Due to the complex social and psychological impacts caused by the \ncondition, people with endometriosis often turn to online health communities \n(OHCs) for support. \n \nObjective: Prior work identifies a lack of large-scale analyses of endometriosis \npatient experiences and of OHCs. Our study fills this gap by investigating aspects of \nthe condition and aggregate user needs that emerge from two endometriosis OHCs, \nr/Endo and r/endometriosis. \n \nMethods: We leverage topic modeling and supervised machine learning to identify \nassociations between a post’s subject matter (“topics”), the people and relationships \n(“personas”) mentioned, and the type of support the post seeks (“intent”).  \n \nResults: The most discussed topics in posts are medical stories, medical \nappointments, sharing symptoms, menstruation, and empathy. In addition, when \ndiscussing medical appointments, users are more likely to mention the endometriosis \nOHCs than medical professionals. Furthermore, medical professional is the least likely \nof any persona to be associated with empathy. Posts that mention partner or family \nare likely to discuss topics from the life issues category, in particular fertility. Lastly, \nwe find that while users seek experiential knowledge regarding treatments and \nhealthcare processes, they also wish to vent and to establish emotional connections \nabout the life-altering aspects of the condition. \n \nConclusions: Endometriosis OHCs provide members a space where they can \ndiscuss care pathways, learn to manage symptoms, and receive validation. Our \nresults emphasize the need for greater empathy within clinical settings, easier \naccess to appointments, more information on care pathways, and further support \nfor patient loved ones. In addition, this study demonstrates the value of quantitative \nanalyses of OHCs: they can support and extend findings from small-scale studies \nabout patient experiences and provide insight into hard-to-reach groups. Lastly, \nanalyses of OHCs can help design interventions to improve care, as argued in \nprevious studies.  \n \nAll rights reserved. No reuse allowed without permission. \n(which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. \nThe copyright holder for this preprintthis version posted February 29, 2024. ; https://doi.org/10.1101/2024.02.27.24303445doi: medRxiv preprint \nNOTE: This preprint reports new research that has not been certified by peer review and should not be used to guide clinical practice.\n\nKeywords: online health communities; patient-centered care; chronic disease; \ninternet; consumer health information; self-help groups; community networks; \ninformation science; social support; \nIntroduction \nEndometriosis \nEndometriosis is a chronic condition that affects 10% of people with a uterus and is \ncharacterized by the presence of uterine lining  tissue  outside of the uterus [1]. This \ncondition causes a range of painful, persistent, and life-altering symptoms, \nincluding, but not limited to chronic pelvic pain, painful menstruation, constipation, \npainful urination, painful sexual intercourse, and infertility. There is no cure for \nendometriosis, so treatment focuses on symptom management and relief [1–3]. \nTreatments might include hormonal therapy, surgical removal of endometriosis, and \nfertility treatment. However, such therapies cause numerous side effects and rarely \nprovide long-term relief to patients [2]. \n \nDue to the absence of condition-specific symptoms and biomarkers, the \nnormalization of menstrual pain, the need for surgery to make a diagnosis, and the \nlack of knowledge about the condition by both the public and clinicians, the average \ntime until diagnosis is estimated to be between 6 to 11 years, depending on the \nhealthcare system of reference [4–6]. A confirmed diagnosis can only be reached \nthrough laparoscopic excision of endometriosis, an invasive surgical procedure [7]. \n \nEndometriosis patients face numerous difficulties during their healthcare journeys. \nNot only do they struggle to find information, but they also encounter negative \nattitudes from physicians [8–10]. Patients’ concerns are often dismissed as ‘just \nperiod pain’ by providers [11]. Negative attitudes seem to derive from physicians’ \nown discomfort with unexplained symptoms [12], as well as from the continued \npresence of hysteria discourse and androcentric views in medical literature [13]. \n \nBecause of these interconnected factors, endometriosis has dire impacts on patients’ \nquality of life [14]. The condition forces people to leave their education and \nemployment and to opt out of social events and everyday activities. Due to sexual \npain and infertility, patients may feel inadequate as partners and fear abandonment \n[15]. \n \nEndometriosis patients necessitate support from partners, family members, and \nfriends to overcome these struggles and to receive a diagnosis [6,16]. Self-care \npractices are time-consuming and labor-intensive for both the patients and their \nloved ones. As patients focus on following complex treatment regimens and become \nexperts in their own care [17–20], a wide range of responsibilities falls onto \npartners and family members. These responsibilities can include financial and \nhousekeeping duties, helping to navigate the healthcare system, and relaying \nmedical information, among others [21–24]. \n \nAll rights reserved. No reuse allowed without permission. \n(which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. \nThe copyright holder for this preprintthis version posted February 29, 2024. ; https://doi.org/10.1101/2024.02.27.24303445doi: medRxiv preprint \n\nResearch on endometriosis highlights several areas of endometriosis care that \nrequire improvement. Medical treatments should be more holistic, taking into \nconsideration the social, emotional, and psychological costs of endometriosis for \nsufferers and their loved ones [1,15,16]. Health care providers should improve their \ncommunication to validate patients’ concerns, meet their informational needs, and \navoid misunderstandings [5,9,18]. Since loved ones are also affected by the \ncondition, they should receive education and training on the condition from \nhealthcare professionals [15,25–27]. \nOnline Health Communities \nOHCs are groups of individuals who come together on an Internet-based platform \n(e.g., social media, website, or forum) to discuss general or condition-specific \nmedical topics. Members may be patients, medical professionals, informal \ncaregivers, patients’ loved ones, or members of the general public [28,29].  \n \nOHCs have been shown to provide support to users who experience dissatisfaction \nor constrained access to medical care, limited social support, or the absence of a \nlocal community of people with the same condition [21]. Indeed, some members join \nOHCs after feeling alienated from the medical community, or becoming distrustful of \nmedical knowledge and care [28,29]. Others join to learn about alternative \ntreatment options, or to advocate for better awareness of their condition [30,31]. \n \nAs these communities allow for varying levels of pseudonymity and anonymity, \nusers with stigmatic and chronic conditions can share intimate or stigmatized \ninformation without fearing social repercussions [32,33]. People with chronic \nconditions often use OHCs to make sense of their experiences and receive validation \n[34,35]. \n \nStudies of OHCs show that an individual member’s support needs may change over \ntime [36,37]. Earlier work on support matching suggests that different types of \nsupport may be more appropriate for certain needs [38]. A study of a breast cancer \nOHC found that the presence of emotional or informational support increased the \noriginal poster’s satisfaction, though users expressed less satisfaction if they \nreceived emotional support when seeking informational support [39]. A separate \nstudy on a mental health OHC found that support matching positively predicted \nsatisfaction, but that there was significant variance across users [36]. \n \nParticipating in OHCs empowers members as they become better informed about \ntheir health concerns, learn to manage their condition, and gain strategies for \ncommunicating with healthcare providers [10,28,40–44]. In many cases, they \nultimately feel less isolated. Contrary to common belief, Huh finds that OHC \nmembers do not share misinformation and commonly invite peers to consult a \nprovider for medical advice [45]. Other studies of OHCs confirm the beneficial \neffects of engaging in these communities, showing that members gradually express \nmore positive emotions than negative ones with sustained participation [46,47]. \nAll rights reserved. No reuse allowed without permission. \n(which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. \nThe copyright holder for this preprintthis version posted February 29, 2024. ; https://doi.org/10.1101/2024.02.27.24303445doi: medRxiv preprint \n\nOne study of an addiction recovery OHC found that engagement in the community \ncorrelates positively with recovery [48]. \n \nResearchers have also highlighted OHCs’ role in the improvement of healthcare \n[49,50]. An existing study of a PCOS subreddit found concordance between trends \nfrom lab results posted to the OHC and trends from clinical research. This indicates \nthat, although OHCs often include patients that are typically excluded from clinical \ntrials (such as those with multiple conditions), studying these communities is useful \nto understand patient populations [51]. Indeed, content analysis of these \ncommunities reveals patterns across patients’ experiences of care and symptoms \n[52–55] and OHC members’ expertise in providing support to peers could be \nleveraged to deliver healthcare interventions and programs [41,43,50,56]. \nEndometriosis Online Communities \nDue to the significant impacts of the condition on patients’ lives, people suffering \nfrom endometriosis often turn to both offline and online communities for help. The \nformer generally consist of dedicated in-person meetings and activities, and access \ndepends on proximity [57,58]. The latter exist in a variety of forms, such as blogs, \nmailing lists, Facebook pages, and Instagram accounts; their activities depend on the \nspecific platform [28,34]. \n \nWhelan et al. find that both an offline and an online endometriosis group are \nepistemic communities. As members share their stories and interact with peers, \nthey build a new epistemology in which patient experiences become valid forms of \nknowledge [59]. \n \nPrevious research also focuses on the kinds of support and content shared in \nendometriosis online communities. In a study of Facebook pages for people with \nendometriosis, Towne et al. show that 48% of posts provided emotional \nsupport, while educational posts made up 21% of the total. Furthermore, they find \nthat 94% of the educational posts shared accurate information [60]. On the other \nhand, Metzler et al. find that most posts on Facebook and Instagram accounts about \nendometriosis offer inspiration or support, awareness about the disease, or \npersonal information. Followers mostly engage with posts that are humorous, \ngenerate awareness, and contain personal content [61]. Finally, Shoebotham and \nCoulson demonstrate that several therapeutic benefits are related to joining \nendometriosis online support groups. They find that members feel reassured and \nempowered while improving their knowledge of endometriosis [62]. \nContribution \nPrior work identifies a lack of large-scale mixed-method analyses on endometriosis \npatient experiences and on online communities. It specifically calls for studies \nregarding: \n• what is discussed in endometriosis online communities [61]; \n• the impact of endometriosis on loved ones and informal caregivers [15]; \n• the impact of endometriosis on adolescents [15]; \nAll rights reserved. No reuse allowed without permission. \n(which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. \nThe copyright holder for this preprintthis version posted February 29, 2024. ; https://doi.org/10.1101/2024.02.27.24303445doi: medRxiv preprint \n\n• the impact of endometriosis-induced infertility [15]; \n \nIndeed, existing qualitative research on patient experiences has been limited to \nsmall patient samples. In contrast, quantitative analyses have used ontologies \ndefined by researchers, rather than inferred from patient narratives [15,60,61]. \nFurthermore, most studies on the effects of endometriosis on quality of life only \ninclude people with an endometriosis diagnosis within the research population (e.g., \n[8,9,11,18,63]). Given the long average delay between symptom onset and diagnosis \n[1,5], many people with endometriosis are missed by this research. \n \nThis study fills this gap by providing a large-scale analysis of user behavior in two \nendometriosis OHCs, r/Endo and r/endometriosis. By studying these communities, \nwe can discover the unmet needs of hard-to-reach groups. Thanks to the \npseudonymity afforded by the platforms, users feel more comfortable discussing \nneeds that they might not have the time or courage to address in clinical settings. In \naddition, these OHCs are open and accessible to anyone, regardless of whether they \nhave a diagnosis or not. As a result, numerous members belong to populations that \nhave been missed by endometriosis research: people who are pre-diagnosis, \nadolescents, and loved ones of people with a diagnosis. \n \nUsing natural language processing, we identify and map the associations between a \npost’s subject matter (“topics”), the people and relationships (“personas”) \nmentioned, and the type of support the post seeks (“intent”). We investigate two \nresearch questions: \n \n• RQ1: What aspects of the endometriosis experience are discussed in OHCs? \n• RQ2: What aggregate needs emerge from the OHCs? \nMethods \nData \nEndometriosis OHCs exist on many platforms in many forms [28,34]. We study two \nthriving endometriosis subreddits, r/Endo and r/endometriosis, which feature high \nmembership and participation numbers (Table 1), and show promise of continued \ngrowth (Figure 1-2). We collect posts and comments from r/Endo and \nr/endometriosis from their inception (January 2012 and November 2014, \nrespectively) to December 2021 using the Pushshift Reddit API . We make available \nthe custom Python code used for the data collection process and subsequent \nanalysis. \n \nTable 1. General statistics of r/Endo, r/endometriosis, and of the combined dataset. \n r/Endo r/endometriosis combined \n    \nNumber of posts 22,584 12,131 34,715 \nAll rights reserved. No reuse allowed without permission. \n(which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. \nThe copyright holder for this preprintthis version posted February 29, 2024. ; https://doi.org/10.1101/2024.02.27.24303445doi: medRxiv preprint \n\nNumber of comments 225,221 127,941 353,162 \nNumber of members 40,734 38,270 79,004 \nUnique posters 20,262 17,150 20,263 \nMean number of words per post 184 182 184 \nMean number of words per \ncomment \n67 66 67 \nMean number of comments per \npost \n8 9 9 \nTotal number of words 19,363,897 10,606,139 29,970,036 \nNumber of unique tokens 110,055 76,379 138,106 \n \nAfter reading posts, examining general statistics of the subreddits (Table 1), and \ncomparing their community-specific languages using Monroe et al.’s Fightin’ Words \nmethod [64] (available in the Appendix A), we find that the two communities share \nsufficient similarities to justify treating them as a single dataset. \n \nFigure 1. Number of posts in r/Endo and r/endometriosis over time (left) and \ndistribution of post lengths by number of words (right). \n  \n \nAll rights reserved. No reuse allowed without permission. \n(which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. \nThe copyright holder for this preprintthis version posted February 29, 2024. ; https://doi.org/10.1101/2024.02.27.24303445doi: medRxiv preprint \n\nFigure 2. Number of comments in r/Endo and r/endometriosis over time (left) and \ndistribution of comments’ lengths by number of words \n(right). \n \nEthical Framing \nPeople with endometriosis have historically been failed by research and medical \ninstitutions. Like other gendered conditions, compared to its severity and the \nnumber of people diagnosed with it, endometriosis is greatly underfunded [65]. \nThere is a persistent imbalance between the high percentage of people with \nendometriosis and the low number of endometriosis experts [1]. Patients deal with \nongoing disbelief, invalidation, or trivialization of their symptoms, even from \nmembers of the medical community [8,10,13]. As academic researchers who are not \nmembers of the endometriosis community, it is imperative that we handle users’ \ndata with care. \n \nThough data from r/Endo and r/endometriosis is public, members of online \ncommunities do not necessarily anticipate that their posts and comments could be \nused by academic researchers [66]. By collecting, analyzing, and publishing research \nabout this data, we extract the data from its intended audience, bringing it to a new, \nunanticipated audience [67]. Following prior examples of handling sensitive, health-\nrelated data [30,52,68], we obscure the source data to protect members from being \nidentified in relation to their posts or comments. Obfuscation is performed in two \nways: 1) throughout this work, we paraphrase any quoted material and 2) we do not \nre-release the underlying text data itself. Any quoted material in the paper has \nundergone rewording at the sentence level to make it less directly searchable, but \nwe retain as much content of the original version as possible. We release all code \nand our codebooks so that other researchers may replicate our results on future \nversions of the OHC, subject to users’ later in situ modifications or deletions of their \ncontributions. \nComputational Text Analysis \nWe use complementary supervised and unsupervised methods to isolate specific \ninstances of personas and intents, but also to allow topics to emerge beyond the \nresearch questions we have designed. \nAll rights reserved. No reuse allowed without permission. \n(which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. \nThe copyright holder for this preprintthis version posted February 29, 2024. ; https://doi.org/10.1101/2024.02.27.24303445doi: medRxiv preprint \n\nTopic Modeling \nFollowing suggestions from research on endometriosis experiences [15], we extract \ntopics from the two endometriosis OHCs using an abductive approach, rather than a \npriori categories. First, we extract topics from posts and comments using \nunsupervised topic modeling. Successively, we evaluate our list of topics against \nthemes previously identified in qualitative research. \n \nTo extract topics from our collection of posts and comments, we use latent Dirichlet \nallocation (LDA) [69], a type of statistical topic modeling. For each topic in the \nmodel, every individual word in the collection is assigned a probability of belonging \nto a given topic. Consequently, each document (e.g., a post or comment) is assigned a \nhigher or lower probability of representing each topic depending on the words it \nfeatures. \n \nBefore training the LDA model, we clean posts and comments using the string \nprocessor included in Antoniak’s little-mallet-wrapper [70], which is \ndesigned to prepare raw text for topic modeling. The string processor splits strings \ninto a series of tokens (words separated by punctuation or spaces), removes \npunctuation and common words, converts all characters to lowercase, and returns \nthe transformed string. After this initial cleaning, we remove any post and comment \nwritten by or responding to bots by searching for the string ‘bot’ in both the user \nname and the text of the document. Next, we implement the LDA function using the \ntomotopy Python package [71]. \n \nWe experiment by running multiple models with different combinations of the \nfollowing parameters: number of topics=10,15,20,25; number of removed most \nfrequent words=5,10,15,20. We also explore training the model with different \ndocument lengths. We first run LDA on whole posts and comments, then we chunk \nthese into paragraphs and sentences. \n \nTo evaluate the performance of each model, we read each topic’s top 100 documents \nby average probability and assign a descriptive label to each topic based on the \ncontent of those documents. \n \nFollowing this evaluation procedure, we find the topic model trained with 25 topics \non paragraph chunks to best suit our purposes. We group topics into 5 overarching \ncategories based on conceptual similarity and interconnectedness: symptoms, \nmedications, healthcare, self-care, and life issues. The detailed description and listing \nof the 5 categories, the 25 topics, and each topic’s top 10 keywords is shown in \nsection 5.1.1 below. We then compare our topics against themes identified in \nprevious research on endometriosis. \nSupervised Classification \nAs a complement to the unsupervised topics, we design two supervised tasks: the \nidentification of people based on their social roles (personas) in posts and the \nAll rights reserved. No reuse allowed without permission. \n(which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. \nThe copyright holder for this preprintthis version posted February 29, 2024. ; https://doi.org/10.1101/2024.02.27.24303445doi: medRxiv preprint \n\nidentification of the goal (intent) of a post. Supervised machine learning allows us to \nassign OHC-specific labels, including personas and intent, to all posts in our dataset.  \n \nPersonas \nPersonas are types of people, organized by social roles, who often interact with a \nperson with endometriosis. We identify discussions of personas in endometriosis \nOHC posts to better understand how endometriosis interfaces with interpersonal \nrelationships. Specifically, we study the four most frequent personas mentioned in \nthe endometriosis OHCs, based on a qualitative analysis of 200 posts: medical \nprofessional, partners, family, and the endometriosis OHCs themselves. Given the \nvariety of terms that could represent each persona (e.g., a gynecologist, a \nsubcategory of medical professional, could also be referred to as gyno, obgyn, \ngynecologist, obstetrician, doctor, doc, provider, or many others), instead of using a \nkeyword search for each persona category, we train a supervised model to identify \npersonas based on hand-labeled examples. \n \nMedical professional is any type of professional in the healthcare system with a \npatient-facing role, such as a doctor, gynecologist, nurse, etc. The partner persona \nincludes romantic partners, and family includes mentions of family members (e.g., \nparents, children, siblings). Depending on paragraph context, family may also \nencompass partners. The endometriosis OHCs label involves the r/Endo and \nr/endometriosis subreddit communities. Paragraphs that mention the subreddit \nmight do so by name, but they also include posts that speak directly to the reader \n(e.g. “can you tell me if you’ve experienced this?”). The endometriosis OHCs label \ndiffers from the others, given that the endometriosis OHCs tends to be both the \naudience and subject matter of a post.  \n \nIn a random sample of paragraphs from posts in the corpus, we assign the \nparagraph a label for every present persona category. If there is no persona present, \nthe paragraph does not receive a label. To assess inter-rater reliability, using the \nlabeling scheme described above (alongside a codebook included in Appendix B), \ntwo authors labeled 200 of the same randomly sampled paragraphs. Using Cohen’s \nkappa, we reach satisfactory inter-rater reliability across all categories. Then, for \neach persona, one author labeled paragraphs until reaching enough labeled data for \nacceptable classification performance, resulting in a different number of total \nparagraphs labeled for each category (Table 2). \n \nTable 2. Number of paragraphs assigned the persona labels out of total paragraphs \nlabeled and inter-rater reliability \nPersona Paragraphs Assigned \nLabel / Total Labeled \nInter-Rater Reliability \n(200 post subset) \n   \nFamily 153/1500 0.79 \nAll rights reserved. No reuse allowed without permission. \n(which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. \nThe copyright holder for this preprintthis version posted February 29, 2024. ; https://doi.org/10.1101/2024.02.27.24303445doi: medRxiv preprint \n\nPartner 166/2000 0.83 \nMedical Professional 349/1000 0.87 \nEndometriosis OHCs 368/1000 0.84 \n \nPersona Models Setup and Prediction \nFor each persona category, we fine-tune a pre-trained DistilBERT model on the \npersona-annotated paragraphs to perform a binary classification task [72]. \nDistilBERT is an English-language large language model that can be fine-tuned on a \ngiven dataset to perform a specific task, such as supervised classification [73]. \nDistilBERT provides a lightweight version of BERT that retains much of its \nperformance, making it easier for other work to replicate our results and to use our \ntrained models. For each persona category, we fine-tune DistilBERT on paragraphs \nfrom both endometriosis OHCs, to best predict the assigned categorical label. We \nkeep all training hyperparameters consistent across models, using a learning rate of \n5e-5, 50 warm-up steps, and a weight decay of 0.01, in three training epochs. As a \nbaseline model, we also perform logistic regression on each persona category, with \ninput texts in term frequency - inverse document frequency (TF-IDF) structure. \nClassification accuracy for a held-out test set of 25% of the total labeled paragraphs \nis listed in Table 3. For all classification results, we present macro scores, which are \na more pessimistic scoring method that treats both classes equally, regardless of \nclass imbalance. We use each trained model to predict instances of personas in \nparagraphs in the rest of the corpus. \n \nTable 3. Classification performance for each persona category, for both logistic \nregression and DistilBERT. All scores are reported as macro averages. \nPersona Classifier Precision Recall F1 \n     \nFamily     \n Logistic Regression 0.50 0.45 0.48 \n DistilBERT 0.94 0.92 0.93 \nPartner     \n Logistic Regression 0.50 0.46 0.48 \n DistilBERT 0.91 0.97 0.93 \nMedical \nProfessional \n    \n Logistic Regression 0.71 0.83 0.72 \nAll rights reserved. No reuse allowed without permission. \n(which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. \nThe copyright holder for this preprintthis version posted February 29, 2024. ; https://doi.org/10.1101/2024.02.27.24303445doi: medRxiv preprint \n\n DistilBERT 0.93 0.93 0.93 \nEndometriosis \nOHCs \n    \n Logistic Regression 0.72 0.83 0.73 \n DistilBERT 0.93 0.92 0.92 \n \nIntent \nPrior research on support in OHCs has established multiple overarching categories \nof support, often characterized as either emotional or informational support \n[36,39,74]. OHC research takes these support categories and maps them onto \nbehavioral features in the data, which suggest the type of support a person seeks or \nprovides [37]. Our work specifically considers what users desire from the act of \nposting, which we call their intent, but we acknowledge that the intent of a post is \nunavailable to researchers without directly speaking to the person who shared a \npost. To develop a set of intent categories that are tailored to the endometriosis \nOHCs, we iteratively label, discuss, and revise our labels.  We identify four common \ncategories of intent: seeking informational support, seeking experiences, seeking \nemotional support, and venting.  \n \nSeeking Informational Support \nSeeking informational support occurs when a person posts to the OHC to find \nmedical information. We build upon prior definitions of seeking informational \nsupport [37,75], but incorporate a novel but simple heuristic for labeling: could the \npost's question be usefully posed to a doctor? After revising the seeking \ninformational support definition, we found major improvements in labeling \nconsistency, speed, and inter-rater reliability. Adding this question also created an \neffective distinction between seeking informational support and seeking experiences. \n \nMy gyno said there’s a chance I have endo, but that I can’t be diagnosed yet \nsince I’m too young (21). Is that true? Is there some sort of test I should be \npushing for? I had a doctor who refused to perform a pelvic exam because she \nsaid I couldn’t have digestive problems because of endo. I’m feeling skeptical \nand I don’t know how to advocate for myself.  \n \nSeeking Experiences \nSeeking experiences is the inverse of seeking informational support}, as posts that  \nseek experiences could only be answered by someone exposed to the endometriosis \nexperience or who has been on the receiving end of care. Posts that seek \nexperiences ask the community for their experiences with a variety of medical \nprocedures or their day-to-day experiences living with endometriosis. Some of these \nposts may also ask if members of the community have experienced similar \nsymptoms.  \n \nAll rights reserved. No reuse allowed without permission. \n(which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. \nThe copyright holder for this preprintthis version posted February 29, 2024. ; https://doi.org/10.1101/2024.02.27.24303445doi: medRxiv preprint \n\nDoes this sound like endo? How did you get your diagnosis? Did you go to a \nspecialist? Any other advice is appreciated. \n \nSeeking Emotional Support \nSeeking emotional support includes posts that ask for encouragement, empathy, \nvalidation, or help navigating emotional situations. These posts may look for \nemotional support after a negative experience, but they may just as easily ask for \ncelebration from the community after a major milestone in care, such as a diagnosis, \nimprovements in symptoms, or successful self-advocacy. \n \nI’m feeling really down and I can’t talk to my doctor. The only reason she \nagreed to do this was because of my mental illness. I’m so afraid that either \noutcome will break my heart. How do I live with the results? \n \nVenting \nOur final label, venting, occurs when a person posts about their grievances living \nwith endometriosis or frustration at a specific situation. We are not aware of similar \nlabels in previous OHC research. Both communities support the practice of venting \nor ranting, and even have “flares” (tags) for posts that vent or rant.  \n \nThis is a long post, but I’m feeling hopeless. I started dealing with things since \naround 12 years old and now I’m 26. This pain has lasted for weeks and I can’t \ndo any of the physical activities that I love and I feel useless and everyone is \ndismissing me like a crazy person. I feel dismissed by today’s doctor, some \nwoman on the phone, all the doctors I’ve ever dealt with since 12. Ugh sorry I \nknow this is long but I needed to rant. Anyway thanks for listening to me talk it \nout. \n \nWe find that most posts begin or end by stating the person’s intent and their \npreferred form of support. Whenever possible, we choose the intent that aligns with \na post’s explicitly stated purpose.  \n \nUsing this codebook (included in Appendix C), one author labeled 1500 sampled \nposts from r/Endo and r/endometriosis, to be used as training data for our models. \nEach post can receive between zero to all four intent category labels, though most \nposts have a primary, explicitly expressed intent. A second author labeled 200 of the \nsame posts as those used for training the models, to be used for measuring inter-\nrater reliability.  Using Cohen’s kappa, we reach acceptable inter-rater reliability \nacross all categories (Table 4).  \n \nTable 4. Number of posts assigned the intent labels out of 1500 posts and inter-rater \nreliability for each label. \nLabel Posts Assigned Label Inter-Rater Reliability \n(200 post subset) \nAll rights reserved. No reuse allowed without permission. \n(which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. \nThe copyright holder for this preprintthis version posted February 29, 2024. ; https://doi.org/10.1101/2024.02.27.24303445doi: medRxiv preprint \n\n   \nSeeking Informational \nSupport \n524 0.79 \nSeeking Experiences 691 0.83 \nSeeking Emotional Support 241 0.76 \nVenting 172 0.74 \n \n \nIntent Models Setup and Prediction \nWe fine-tune a series of DistilBERT models to perform binary classification to \npredict each intent category in a post. Classification accuracy for a held-out test set \nof 25% of the total labeled paragraphs is listed in table 5. Overall, the intent models \nreach acceptable performance, though it is lower than that of our persona models. \nThis slightly lower performance is expected because of the more complex nature of \nthe intent categories. We then use the fine-tuned models to predict the intent of \nposts in the entire corpus.  \n \nTable 5. Classification performance for each intent category, for both logistic \nregression and DistilBERT. \nIntent Classifier Precision Recall F1 \n     \nSeeking Informational \nSupport \n    \n Logistic Regression 0.59 0.71 0.56 \n DistilBERT 0.86 0.82 0.84 \nSeeking Experiences     \n Logistic Regression 0.75 0.75 0.75 \n DistilBERT 0.83 0.83 0.83 \nSeeking Emotional \nSupport \n    \n Logistic Regression 0.51 0.80 0.47 \n DistilBERT 0.72 0.69 0.70 \nVenting     \nAll rights reserved. No reuse allowed without permission. \n(which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. \nThe copyright holder for this preprintthis version posted February 29, 2024. ; https://doi.org/10.1101/2024.02.27.24303445doi: medRxiv preprint \n\n Logistic Regression 0.50 0.44 0.47 \n DistilBERT 0.83 0.80 0.81 \nResults \nRQ1: What aspects of the endometriosis experience are discussed in OHCs? \nLeveraging topic probabilities, we investigate which aspects of endometriosis \nexperiences are discussed in the endometriosis OHCs. We first consider what topics \nemerge from the endometriosis OHCs once we perform LDA topic modeling on \nparagraph chunks from posts and comments. Secondly, we analyze which of those \ntopics are the most discussed in posts. \nTopics in Posts and Comments \nEmploying LDA topic modeling, we find discussions of five main topic categories in \nthe endometriosis OHCs: symptoms, medications, healthcare, self-care practices and \nlife issues. A complete list of the five categories and our 25 topics is provided below. \nAlthough healthcare is the category with the highest number of topics, symptoms \nand life issues are also largely discussed in these communities. We also find the \nimportance of self-care practices, as they are discussed substantially enough that we \nplace them in a separate category. \n \nSymptoms \nA major pattern in the two OHCs is the presence of topics related to symptoms \n(Table 6). People with endometriosis suffer from a wide range of disabling chronic \nsymptoms: gastrointestinal issues; pelvic floor pain; heavy, irregular, and painful \nmenstruation; muscular cramps in their legs and abdomen. Many users share these \nsymptoms with the communities in hope of receiving or providing support.  \n \nTable 6. Topics in the symptoms category. Numbers are assigned randomly by the \nmodel, while labels are assigned upon reading 100 documents for each topic. \nTopic # Label Top 10 words \n   \n0 Gastrointestinal take nausea bowel stomach help water constipation \ntaking drink helps  \n3 Pelvic floor pelvic floor therapy physical help sex helped \ntherapist muscles lot \n5 Menstruation period periods days bleeding symptoms painful \nheavy cramps started normal \n17 Muscular back sex right feel feels lower side sometimes left \npainful \nAll rights reserved. No reuse allowed without permission. \n(which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. \nThe copyright holder for this preprintthis version posted February 29, 2024. ; https://doi.org/10.1101/2024.02.27.24303445doi: medRxiv preprint \n\n21 Sharing \nsymptoms \nfeel period day days bad time every worse back last \n \nMedications \nDue to the chronic nature and current incurability of endometriosis, people with \nendometriosis make use of a variety of drugs and treatments. Users of the two OHCs \noften list their pain management routine , share hormonal treatment experiences \n(“18 months ago I started using the Nuva ring and I love it.”), recount the side effects \nof specific drugs they have used, or provide medical information on hormonal drugs \n(“Orlissa is a GnRH antagonist, so it lowers estrogen directly without relying on the \nsame feedback mechanism as Lupron”). The medications category groups these \nexperiences (Table 7). \n \nTable 7. Topics in the medications category. Numbers are assigned randomly by the \nmodel, while labels are assigned by reading the top 100 documents for each topic. \nTopic # Label Top 10 words \n   \n14 Pain \nmanagement \nwork take cbd time day job days help use much \n18 Hormonal drug \nexperiences \ncontrol months birth iud pill period years mirena \nperiods got \n23 Drugs take side taking effects weight months pill \nmedication dose NUMmg \n24 Information on \nhormonal drugs \ncontrol birth symptoms treatment side estrogen \neffects hormones lupron hormonal \n \nHealthcare \nIn the healthcare category, we group topics regarding the medical aspects of \nendometriosis, and how endometriosis patients experience the healthcare system \n(Table 8). Often, senior members of the OHCs provide new users with medical \ninformation on the condition, overviews on the process of getting diagnosed, as well \nas information on surgery. Users also advise each other on how to prepare for their \nmedical appointments. They often point to competent endometriosis specialists, \ncompare insurance policies, and highlight helpful online resources. \n \nTable 8. Topics in the healthcare category. Numbers are assigned randomly by the \nmodel, while labels are assigned upon reading the top 100 documents for each topic. \nTopic # Label Top 10 words \n   \nAll rights reserved. No reuse allowed without permission. \n(which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. \nThe copyright holder for this preprintthis version posted February 29, 2024. ; https://doi.org/10.1101/2024.02.27.24303445doi: medRxiv preprint \n\n1 Information on \nsurgery \nlap excision weeks first back still time two \nmonths pos \n2 Medical information cyst ovary uterus endometriosis cysts removed \ntissue ovaries ultrasound found \n4 Getting diagnosed symptoms blood could ultrasound bladder test \nissues tests doctor endometriosis \n6 Online resources nook endometriosis https nancy group //www \nresearch facebook list doctors \n9 Specialists doctor specialist find see excision doctors one \nendometriosis good need \n13 Insurance insurance medical health hospital work care pay \nneed doctor live \n20 Medical \nappointments \ndoctor going ask see thank anyone appointment \nsure want think \n \nSelf-care \nAs endometriosis requires a considerable amount of self-care (Table 9), patients are \nfaced with the challenge of caring for themselves while also having work and other \nresponsibilities. Users of the OHCs find support against exhaustion and isolation by \ncomparing experiences and tips about their post surgery recovery. They also provide \ndetailed information on their diet, product recommendations for gadgets that help \nwith daily activities, and various comfort items for when symptoms flare-up. \n \nTable 9. Topics in the self-care category. Numbers are assigned randomly by the \nmodel, while labels are assigned upon reading the top 100 documents for each topic. \nTopic # Label Top 10 words \n   \n8 Post surgery \nrecovery \nday days first home time around back gas hours \nweek \n15 Product \nrecommendations \nheating pad use hot heat one water help helps \npads \n19 Diet diet eat gluten food foods dairy eating try free lot \n22 Comfort items wear pants belly look weight one size wearing \nsuper cup \n \n \nAll rights reserved. No reuse allowed without permission. \n(which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. \nThe copyright holder for this preprintthis version posted February 29, 2024. ; https://doi.org/10.1101/2024.02.27.24303445doi: medRxiv preprint \n\nLife issues \nThe last category, life issues, groups users’ discussions of general life issues \nconnected with having a severe chronic condition (Table 10). In these communities, \nusers open up about their experiences of dismissal and abuse  and their medical \nstories as patients. They give each other support through their fertility struggles. \nCommunity members exchange expressions of gratitude and empathy with their \npeers. \n \nTable 10. Topics in the life issues category. Numbers are assigned randomly by the \nmodel, while labels are assigned upon reading the top 100 documents for each topic. \nTopic # Label Top 10 words \n   \n7 Dismissal people even doctors think feel something say one \nwant women \n10 Gratitude hope thank good much sorry better feel luck well \nfind \n11 Medical stories years told doctor said got went diagnosed back \nlap finally \n12 Fertility pregnant want kids years hysterectomy fertility \npregnancy \n16 Empathy feel people life want help much need support \nsorry hard \nMost Discussed Topics in Posts \nTo investigate which aspects of endometriosis patient experiences are most \ndiscussed in the endometriosis OHCs, we measure which topics have the highest \naverage probability in all posts. Indeed, if a topic shows a high average probability \nacross all posts, it indicates that the topic is highly present in the endometriosis \nOHCs. In posts, the topics with the highest average probability are medical stories, \nmedical appointments, sharing symptoms, menstruation and empathy (Table 11, \nFigure 3).  \n \nTable 11. Topics with highest average probability in posts \nTopic # Label Average probability \n   \n11 Medical stories 0.086 \n20 Medical \nappointments \n0.081 \nAll rights reserved. No reuse allowed without permission. \n(which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. \nThe copyright holder for this preprintthis version posted February 29, 2024. ; https://doi.org/10.1101/2024.02.27.24303445doi: medRxiv preprint \n\n21 Sharing symptoms 0.080 \n5 Menstruation 0.079 \n16 Empathy  0.067 \n \nWe find that medical stories and medical appointments are the two most discussed \ntopics. New or returning users frequently recount their healthcare journey at the \nbeginning of their posts: from having the first symptoms as teens, to undergoing \nsurgery, and choosing between treatment options. Other times, users ask specific \nquestions on how to book their medical appointment, what to do if an appointment \nis moved or the physician does not show up, and what strategies others use to \ncommunicate successfully with their doctors. \n \nTwo symptoms topics, sharing symptoms and menstruation, are among the most \npresent topics. Users of the endometriosis OHCs share detailed accounts of all their \nsymptoms in order to gain their peers’ opinions on whether they should seek urgent \ncare, whether a new symptom might be caused by their treatment rather than \nendometriosis, and whether what they are going through resembles other people’s \nendometriosis. \n \nFigure 3. Average topic probabilities in posts collected from the two endometriosis \nOHCs ordered by the categories. Medical appointments, medical stories, sharing \nsymptoms, menstruation, and empathy have the highest average probability. \n \n \nAll rights reserved. No reuse allowed without permission. \n(which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. \nThe copyright holder for this preprintthis version posted February 29, 2024. ; https://doi.org/10.1101/2024.02.27.24303445doi: medRxiv preprint \n\nA large number of posts in the OHCs are solely dedicated to describing menstrual \nsymptoms. New users of these communities are often undiagnosed teenagers who \nwonder whether they should seek medical assistance given their experiences with \nmenstruation. Furthermore, endometriosis is typically treated with hormonal \nmedicines, which cause additional changes to patients’ menstrual cycles. Patients \nshare such changes with peers to understand if the treatment has been effective at \nrelieving their pain. \n \nEmpathy is the fifth most present topic in posts of the two OHCs, underlining that \ndemonstrations of empathy are extremely valued by endometriosis patients. Sadly, \nusers often lament feeling misunderstood and dismissed. \nRQ2: What aggregate needs emerge from the OHCs? \nIn this section, we consider the needs expressed by members of the OHCs. For each \nof the topics outlined in RQ1, we consider 1) which topics are more likely when \ndifferent personas are mentioned and 2) what the intent of posts are when they \nmention each topic. By doing so, we can better understand the interplay between \nendometriosis experiences, interpersonal relationships, and the goals of OHC \nmembers.  \n \nPersonas in the OHCs \nOf the four persona categories, posts to the endometriosis OHC most often mention \nthe endometriosis OHCs, followed by medical professional, family, and partners \n(Figure 4).  \n \nAll rights reserved. No reuse allowed without permission. \n(which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. \nThe copyright holder for this preprintthis version posted February 29, 2024. ; https://doi.org/10.1101/2024.02.27.24303445doi: medRxiv preprint \n\nFigure 4. Percentage of posts with more than one mention of each persona in the \nendometriosis OHCs. \n \n \nOf posts predicted with at least one of the four personas, we find which topics are \nmost present. For each persona, we find the average topic probabilities for all posts \npredicted with each persona, converted to z-scores. Figure 5 displays this result, \ndepicting what members of the OHCs are most likely to discuss when they mention \neach persona.  \n \nAll rights reserved. No reuse allowed without permission. \n(which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. \nThe copyright holder for this preprintthis version posted February 29, 2024. ; https://doi.org/10.1101/2024.02.27.24303445doi: medRxiv preprint \n\nFigure 5. Average topic probabilities (converted to z-scores) for posts with different \npersonas.  \n \n \nWhen a medical professional is mentioned, posts are more likely about medical \nappointments and medical stories, highlighting the important role that providers \nhave in shaping patient medical pathways. However, medical professional is the least \nlikely of any persona to be discussed in combination with empathy (P<.001). \n \nInterestingly, posts with the endometriosis OHCs are more likely to discuss medical \nappointments than posts with medical professional (P<.001). In alignment with our \nfindings in RQ1, users of the OHC request the assistance of the community to \nprepare for visits, as this support might not be available to them in clinical settings. \n \nPosts that mention partner or family are likely to discuss topics from the life issues \ncategory, in particular fertility (P<.001). These posts emphasize how navigating \nfertility deeply affects relationships. Mentions of family in posts about fertility may \nhave to do with family planning and personal goals in growing a family. Some may \nexpress concern about being able to have or keep a partner when dealing with \ninfertility. These posts also mention feeling pressure to have children from family or \npartners.  \n \nLastly, posts that mention partner often also discuss post surgery recovery (P<.001). \nPartners can indeed play an important role in helping endometriosis patients access \ntreatment and maintaining self-care routines. In addition, it is sometimes the \npartner of a person with endometriosis who asks for advice from the OHC. \n \nAll rights reserved. No reuse allowed without permission. \n(which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. \nThe copyright holder for this preprintthis version posted February 29, 2024. ; https://doi.org/10.1101/2024.02.27.24303445doi: medRxiv preprint \n\nIntents in the OHCs \nWe then consider the goals of members of the community in their posts, through our \nintent predictions. Across all posts, we find that users are most likely to seek \nexperiences from the OHC; they do so in roughly half of posts. Seeking informational \nsupport occurs in around a quarter of posts, and seeking emotional support and \nventing are the least common intent types, based on our model (Figure 6).  \n \nFigure 6. Percentage of posts with each intent label in the endometriosis OHCs. \n \n \nWe then find the average topic probabilities for posts with each predicted intent \ncategory. By doing so, we can find which subjects are most related to different goals. \nWhen a member seeks information from the community, what are they trying to \nlearn about? When a member simply wants to vent, what subjects are most often \nrelated to their frustration?  \n \nAll rights reserved. No reuse allowed without permission. \n(which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. \nThe copyright holder for this preprintthis version posted February 29, 2024. ; https://doi.org/10.1101/2024.02.27.24303445doi: medRxiv preprint \n\nFigure 7. Average topic probabilities (converted to z-scores) for posts with different \nintents.  \n \n \nWe find an important divide between the subject matter of posts that seek \nexperiences or informational support and those that seek emotional support or vent \n(Figure 7). The subject matter of posts that seek information or experiences are more \noften about topics in the symptoms, medications, and healthcare categories. While \nmembers more likely seek emotional support and vent about the life issues topics, \nincluding dismissal, medical stories, fertility, and empathy.  \n \nHowever, members of the endometriosis OHCs do seek emotional support – and vent \n– about pain management and when sharing symptoms. While a person with \nendometriosis might look for information or experiences regarding their symptoms \nand pain, they are more likely to look for emotional support from the community or \nto vent their frustrations.  \nDiscussion \nRQ1: What aspects of the endometriosis experience are discussed in OHCs \nUsing topic modeling we find that OHCs are spaces dedicated to narrations of users’ \nhealthcare pathways, directions on how to find care and manage symptoms, as well \nas expressions of validation between peers regarding their health concerns.  \n \nIn particular, the most discussed topics in the two communities are medical stories, \nmedical appointments, sharing symptoms, menstruation, and empathy. These results \nalign with previous findings from qualitative studies collecting endometriosis \npatients' experiences. These include evidence of the benefits of sharing one’s story \nAll rights reserved. No reuse allowed without permission. \n(which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. \nThe copyright holder for this preprintthis version posted February 29, 2024. ; https://doi.org/10.1101/2024.02.27.24303445doi: medRxiv preprint \n\nwithin community [34,59], the need for assistance with treatment regimens and \nappointments [10,63], the uncertainty experienced by patients related to their \nsymptomatology [11], as well as the value of receiving validation regarding health \nconcerns and symptoms [26]. \n \nAn existing study of a PCOS subreddit has also found concordance between the OHC \nuser population and research-identified patient cohorts [51]. Although the PCOS \nOHC includes patients that are typically excluded from clinical trials (such as those \nwith multiple conditions), trends found in laboratory test results posted to the \ncommunity are consistent with clinically reported results. \n \nOur results also align with studies on OHCs, showing that OHC users become better \nat communicating with their providers and at managing their conditions, as well as \nfeel less isolated [28,34,35,40,42–44].  \nRQ2: What aggregate needs emerge from the OHCs? \nUsing supervised classification of personas and intents we find that posts mention \nthe endometriosis OHCs more than they mention medical professionals – \nhighlighting the vital role that these groups play in the users’ healthcare decisions –, \nand that the majority of posts are written to seek experiential advice. Venting is the \nleast common of our intent categories, but venting still occurs in a substantial \nfraction (10%) of posts. \n \nCombining these classification models with unsupervised topic models, we find that \nusers need assistance with accessing and preparing for medical visits, as well as \nnavigating fertility options. To meet these needs, patients currently turn to the \nOHCs, their partners, and their family. Interestingly, members of the OHCs seldomly \nassociate medical professionals and providers with empathy.  \n \nWe also find that patients’ relationships with their partners and family members can \nbe affected by the condition. Users share how physical manifestations of \nendometriosis, such as infertility, alter their life goals and complicate personal \nrelationships. At the same time, partners and family members play a vital role of \nserving as informal caregivers. These personas even use the OHCs for advice in \ncreating a strong support system.  \n \nFurthermore, while users seek experiential knowledge regarding treatments and \nhealthcare processes, they also wish to vent and establish an emotional connection \nabout the life-altering aspects of the condition. \n \nThese results align with previous research on the areas of endometriosis care that \nneed improvement, including non-holistic treatments [1,15,16], unsatisfactory \npatient-provider communication [5,9,18], and lack of training or educational \nresources for of patients’ loved ones [15,25–27]. \nAll rights reserved. No reuse allowed without permission. \n(which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. \nThe copyright holder for this preprintthis version posted February 29, 2024. ; https://doi.org/10.1101/2024.02.27.24303445doi: medRxiv preprint \n\nConclusions \nIn this study, we conduct a large-scale analysis of user needs in two endometriosis \nOHCs, r/Endo and r/endometriosis. We find that these communities provide \nmembers a space where they can discuss care pathways, learn to manage \nsymptoms, and receive validation. Our results also point to the need for greater \nempathy within clinical settings, easier access to appointments, more information \non healthcare processes, and further support to patient loved ones.  \n \nOur study demonstrates the value of quantitative analyses of OHCs. OHCs provide \nvery large datasets on patient experiences. In this work, we analyzed hundreds of \nthousands of posts and comments by tens of thousands of users. This sample size is \nan order of magnitude larger than that examined in any other study of \nendometriosis patient needs and experiences of which we are aware. Our results \nthus fortify findings from small-scale studies about patient experiences and provide \ninsight into hard-to-reach groups.  \n \nLastly, we believe that studies of OHCs can help design interventions to improve \ncare, as argued in previous studies [30,49,51,52].  \nAuthor’s Contributions \nFB conducted topic modeling, data analysis, curated visualizations, wrote and \nrevised the manuscript. \nRT conducted supervised classification, data analysis, curated visualizations, wrote \nand revised the manuscript. \nKP provided clinical insight into endometriosis symptoms, diagnosis and treatment \nand revised the manuscript.  \nMW provided advice and guidance through each step of the project from designing \nthe experiments to revising the manuscript. \nConflicts of Interest \nNone declared. \nAbbreviations \nOHC: Online health community \nReferences \n1.  Zondervan KT, Becker CM, Missmer SA. Endometriosis. Longo DL, ed. 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