Training factors that influence electrodiagnostic medicine knowledge
Bibliographic record
Abstract
INTRODUCTION/AIMS: Self-assessment examinations (SAEs) help trainees assess their progress in education. SAEs also provide feedback to training programs as to how factors in training influence examination performance. This study's goal was to examine the relationship between the number of months of training in electrodiagnostic (EDx) medicine, the number of EDx studies during training, and scores on the American Association of Neuromuscular and Electrodiagnostic Medicine SAE. METHODS: This was a retrospective study of the 2023 AANEM-SAE results. In addition to the examination score, participants were asked approximately how many EDx studies they performed in training and how many months of training they had completed. Analysis included correlation of the examination scores with months of training as well as number of EDx studies. In addition, a multivariate linear regression model was developed. RESULTS: A total of 756 participants completed the proctored examination in May 2023. Examination score was moderately and positively correlated with the number of months of training (Pearson r = .5; p < .001) as well as the number of EDx studies during training (Pearson r = .55; p < .001). Scores steadily improved with additional months of training, but leveled off after 300-400 EDx studies. Regression analysis indicated that higher numbers of EDx studies were correlated with a higher examination score even after accounting for the number of months of study. DISCUSSION: We believe that a greater number of months of training is associated with better performance on the AANEM-SAE and that greatest improvement in examination performance occurs during the first 300-400 EDx studies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".