Rheumatology Image of the Month: A Low Resource Innovation With Measurable Results
Bibliographic record
Abstract
OBJECTIVES: Strategies to increase confidence in rheumatology knowledge are valuable for medical trainees and residents. A web-based teaching innovation was implemented in an attempt to increase rheumatology exposure for internal medicine residents. METHODS: An Image of the Month webpage was established, where a practicing rheumatologist would post a new image that could be answered online by internal medicine residents. Cumulative data was analyzed to determine the extent and change in rheumatology exposure. RESULTS: The Image of the Month webpage posted images for a total of 76 months between July 2010 to May 2017, with a total of 1326 submitted responses. The proportion of residents who only participated in Image of the Month and only did a rheumatology rotation averaged 36.1% and 16.5%, respectively. The proportion of residents who only participated in Image of the Month was higher than the proportion who only did a rheumatology rotation for all of the 7 time periods assessed. A total of 491 residents participated in Image of the Month, with an average of 54.9% of residents participating each year. Overall, on average, 52 residents had 1 or more submissions, 3.6 entries were submitted per resident, and 17.4 entries were submitted per month. Junior residents (PGY1) participated more often than senior residents (PGY3). CONCLUSIONS: The Image of the Month webpage successfully improves internal medicine resident exposure to rheumatology with minimal resources and manpower required. Further study is necessary to determine the impact this exposure may have on the abilities and confidence levels of internal medicine residents.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".