The Quality of Dermatology Match Information on Social Media Platforms: Cross-Sectional Analysis
Bibliographic record
Abstract
The dermatology match is a highly discussed topic, and creators online often comment about the qualifications needed to match.Social media posts can share differing advice with potential misinformation that can cause excess stress and deter students interested in dermatology.We aimed to assess the quality of online information about the match process, with sources from Reddit, Student Doctor Network (SDN), and TikTok.In March 2024, "How to match into dermatology" and "Advice for the dermatology match process" was searched on these platforms, and 34 sources were assessed for the information source and specific advice for application components such as USMLE scores, research experiences and clinical interests, clerkship grades, and away rotations.Online material was compared with the official National Residency Matching Program (NRMP) 2022 match data using student t-test's for mean USMLE scores and research experiences.We collected information on media recommendations regarding research years, clerkship grades, medical school rankings, AOA status, broad vs focused dermatology and volunteer interests, away rotations, and dual applications.10 Tik-Tok videos, 15 Reddit posts, and 10 SDN posts were included in the study.The NRMP and online media data differed significantly among mean Step 1 scores (248 vs 254.5, p<0.001), number of abstracts, posters, and publications (20.9 vs 23.3, p<0.01), and number of total publications (7 vs 13.2, p<0.001).The NRMP and online data did not differ significantly among mean Step 2 scores (257 vs 261.0, p=0.06).Of the 22 total articles that discussed a potential research year, 16 articles recommended taking a research year during medical school (72.7%).Fifteen total articles mentioned grades during medical school, and 10 of these articles explained the importance of attaining AOA status (66.7%), compared to the NRMP data which shared that 39.7% of matched dermatology residents attained AOA status.Six articles commented on students pursuing focused vs broad interests in dermatology, and 3 (50%) articles recommended having broad interests in dermatology, while 3 (50%) discussed having niche interests.Twenty-one articles covered the topic of away rotations, of which 19 (90.5%) recommended doing an away rotation.Eleven articles discussed a number (mean 3.9 away rotations), whereas 8 (38.0%) articles said to complete as many away rotations as possible, in contrast to the official APD letter which recommends completing no more than 2 away rotations.Media found online does not match the AAMC-verified data or current APD statements.Accredited programs should consider releasing a statement regarding match information to dispel common rumors.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".