A Systematic Review of the Relationship Between In-Training Examination Scores and Specialty Board Examination Scores
Bibliographic record
Abstract
BACKGROUND: In-training examinations (ITEs) are intended for low-stakes, formative assessment of residents' knowledge, but are increasingly used for high-stake purposes, such as to predict board examination failures. OBJECTIVE: The aim of this review was to investigate the relationship between performance on ITEs and board examination performance across medical specialties. METHODS: A search of the literature for studies assessing the strength of the relationship between ITE and board examination performance from January 2000 to March 2019 was completed. Results were categorized based on the type of statistical analysis used to determine the relationship between ITE performance and board examination performance. RESULTS: Of 1407 articles initially identified, 89 articles underwent full-text review, and 32 articles were included in this review. There was a moderate-strong relationship between ITE and board examination performance, and ITE scores significantly predict board examination scores for the majority of studies. Performing well on an ITE predicts a passing outcome for the board examination, but there is less evidence that performing poorly on an ITE will result in failing the associated specialty board examination. CONCLUSIONS: There is a moderate to strong correlation between ITE performance and subsequent performance on board examinations. That the predictive value for passing the board examination is stronger than the predictive value for failing calls into question the "common wisdom" that ITE scores can be used to identify "at risk" residents. The graduate medical education community should continue to exercise caution and restraint in using ITE scores for moderate to high-stakes decisions.
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 | 0.012 | 0.091 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.015 | 0.019 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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; 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".