How Robust Are Studies in the American Board of Emergency Medicine Maintenance of Certification Lifelong Learning and Self‐assessment? An Examination of Fragility and Bias of Included Randomized Controlled Trials
Bibliographic record
Abstract
OBJECTIVE: Critics have raised concerns regarding the validity of maintenance of certification (MOC) programs. We sought to examine the quality of the randomized controlled trials (RCTs) selected for the lifelong learning and self-assessment (LLS) component of the American Board of Emergency Medicine (ABEM) MOC program. METHODS: We systematically reviewed the ABEM LLS reading lists from 2004 to 2017 to identify RCTs with dichotomous outcomes and superiority designs. A fragility index (FI) was calculated using Fisher's exact test for all statistically significant dichotomous outcomes. Bivariate correlation was performed to examine associations between the FI and RCT study characteristics. Each included study was evaluated with the Cochrane Collaboration risk-of-bias (ROB) tool. RESULTS: Thirteen superiority RCTs with dichotomous outcomes were included in the 2004-2017 LLS reading lists. Ten had a statistically significant outcome, and the majority were robust and at low ROB. The median trial size was 511 patients (interquartile range [IQR] = 251-1,517), and the median FI was 10 (IQR = 7-18); i.e., if 10 patients in the treatment arm had not had events, the results would not have been statistically significant. CONCLUSIONS: The majority of RCTs included in the LLS are robust and at low ROB.
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 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 | 0.644 | 0.881 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.015 | 0.025 |
| Bibliometrics | 0.028 | 0.020 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.016 | 0.014 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.008 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".