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Record W4400099565 · doi:10.2196/50141

Understanding Pediatric Experiences With Symptomatic Varicoceles: Mixed Methods Study of an Online Varicocele Community

2024· article· en· W4400099565 on OpenAlexvenueno aff
Grace E Sollender, Tommy Jiang, Ilana Finkelshtein, Vadim Osadchiy, Michael Zheng, Jesse N. Mills, Jennifer Singer, Sriram V. Eleswarapu

Bibliographic record

VenueJMIR Formative Research · 2024
Typearticle
Languageen
FieldHealth Professions
TopicMale Reproductive Health Studies
Canadian institutionsnot available
Fundersnot available
KeywordsVaricocelePreprintMedicineComputer scienceInfertilityBiologyWorld Wide Web

Abstract

fetched live from OpenAlex

BACKGROUND: Varicoceles affect up to 30% of postpubertal adolescent males. Studying this population remains difficult due to this topic's sensitive nature. Using the popularity of social media in this cohort and natural language processing (NLP) techniques, our aim was to identify perceptions of adolescent males on an internet varicocele forum to inform how physicians may better evaluate and counsel this pediatric population. OBJECTIVE: We aimed to characterize themes of discussion and specific concerns expressed by adolescents using a mixed methods approach involving quantitative NLP and qualitative annotation of an online varicocele community. METHODS: We extracted posts from the Reddit community "r/varicocele" (5100 members) with criteria of discussant age ≤21 years and word count >20. We used qualitative thematic analysis and the validated constant comparative method, as well as an NLP technique called the meaning extraction method with principal component analysis (MEM/PCA), to identify discussion themes. Two investigators independently interrogated 150 randomly selected posts to further characterize content based on NLP-identified themes and calculated the Kaiser-Meyer-Olkin (KMO) statistic and the Bartlett test. Both quantitative and qualitative approaches were then compared to identify key themes of discussion. RESULTS: A total of 1103 posts met eligibility criteria from July 2015 to June 2022. Among the 150 randomly selected posts, MEM/PCA and qualitative thematic analysis separately revealed key themes: an overview of varicocele (40/150, 27%), management (29/150, 19%), postprocedural experience (28/150, 19%), seeking community (26/150, 17%) and second opinions after visiting a physician (27/150, 18%). Quantitative analysis also identified "hypogonadism" and "semen analysis" as concerns when discussing their condition. The KMO statistic was >0.60 and the Bartlett test was <0.01, indicating the appropriateness of MEM/PCA. The mean age was 17.5 (SD 2.2; range 14-21) years, and there were trends toward higher-grade (40/45, 89% had a grade of ≥2) and left-sided varicoceles. Urologists were the topic of over 50% (53/82) of discussions among discussants, and varicocelectomy remained the intervention receiving the most interest. A total of 60% (90/150) of discussants described symptomatic varicoceles, with 62 of 90 reporting pain, 24 of 90 reporting hypogonadism symptoms, and 45 of 90 reporting aesthetics as the primary concern. CONCLUSIONS: We applied a mixed methods approach to identify uncensored concerns of adolescents with varicoceles. Both qualitative and quantitative approaches identified that adolescents often turned to social media as an adjunct to doctors' visits and to seek peer support. This population prioritized symptom control, with an emphasis on pain, aesthetics, sexual function, and hypogonadism. These data highlight how each adolescent may approach varicoceles uniquely, informing urologists how to better interface with this pediatric population. Additionally, these data may highlight the key drivers of decision-making when electing for procedural management of varicoceles.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.011
metaresearch head score (Gemma)0.019
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.061

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.019
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0020.002
Scholarly communication0.0020.003
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.582
GPT teacher head0.630
Teacher spread0.049 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations1
Published2024
Admission routes1
Has abstractyes

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