An evaluation of accessibility and content of microsurgery fellowship websites
Bibliographic record
Abstract
BACKGROUND: Websites for residency and fellowship programs serve as effective educational and recruitment tools. OBJECTIVE: To evaluate the accessibility and content of fellowship websites that are commonly used by microsurgery applicants for career development. METHODS: tests and ANOVA (two-tailed; P<0.05 was considered to be statistically significant). RESULTS: A list of 53 eligible programs was compiled. Only 15 of 51 (29%) ASRM program links were functional. On average, the combined content from ASRM website and individual MFWs had 2.91 of 6 recruitment variables and 1.32 of 6 education variables, respectively. The majority of programs listed 'eligibility criteria' (87%) and 'general information' (87%). 'Evaluation criteria' were most poorly reported (4%). Recruitment score was higher for United States programs compared with international counterparts (51% versus 33%, respectively; P=0.02). It was also higher in programs that focus on 'extremity' versus 'breast' (58% versus 37%; P=0.0028). Education scores did not differ according to location, program size, subspecialty of focus or participation in the Microsurgery Match process. CONCLUSION: Information regarding recruitment and education on most MFWs is scarce. Academic institutions should keep website content up to date and comprehensive to better assist candidates in the application process.
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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 | 0.006 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".