Key‐feature questions for assessment of clinical reasoning: a literature review
Bibliographic record
Abstract
OBJECTIVES: Key-feature questions (KFQs) have been developed to assess clinical reasoning skills. The purpose of this paper is to review the published evidence on the reliability and validity of KFQs to assess clinical reasoning. METHODS: A literature review was conducted by searching MEDLINE (1946-2012) and EMBASE (1980-2012) via OVID and ERIC. The following search terms were used: key feature; question or test or tests or testing or tested or exam; assess or evaluation, and case-based or case-specific. Articles not in English were eliminated. RESULTS: The literature search resulted in 560 articles. Duplicates were eliminated, as were articles that were not relevant; nine articles that contained reliability or validity data remained. A review of the references and of citations of these articles resulted in an additional 12 articles to give a total of 21 for this review. Format, language and scoring of KFQ examinations have been studied and modified to maximise reliability. Internal consistency reliability has been reported as being between 0.49 and 0.95. Face and content validity have been shown to be moderate to high. Construct validity has been shown to be good using vector thinking processes and novice versus expert paradigms, and to discriminate between teaching methods. The very modest correlations between KFQ examinations and more general knowledge-based examinations point to differing roles for each. Importantly, the results of KFQ examinations have been shown to successfully predict future physician performance, including patient outcomes. CONCLUSIONS: Although it is inaccurate to conclude that any testing format is universally reliable or valid, published research supports the use of examinations using KFQs to assess clinical reasoning. The review identifies areas of further study, including all categories of evidence. Investigation into how examinations using KFQs integrate with other methods in a system of assessment is needed.
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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 | 0.019 | 0.076 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.020 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".