Comparison of an Aggregate Scoring Method With a Consensus Scoring Method in a Measure of Clinical Reasoning Capacity
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Diversity of clinical reasoning paths of thought among experts is well known. Nevertheless, in written clinical reasoning assessment, the common practice is to ask experts to reach a consensus on each item and to assess students on a unique "good answer." PURPOSES: To explore the effects of taking the variability of experts answers into account in a method of clinical reasoning assessment based on authentic tasks: the Script Concordance Test. METHODS: Two different methods were used to build answer keys. The first incorporated variability among a group of experts (criterion experts) through an aggregate scoring method. The second was made with the consensus obtained from the group of criterion experts for each answer. Scores obtained with the two methods by students and another group of experts (tested experts) were compared. The domain of assessment was gynecology-obstetric clinical knowledge. The sample consisted of 150 clerkship students and seven other experts (tested experts). RESULTS: In a context of authentic tasks, experts' answers on items varied substantially. Amazingly, 59% of answers given individually by criterion group experts differed from the answer they provided when they were asked in a group to provide the "good answer" required from students. The aggregate scoring method showed several advantages and was more sensitive to detecting expertise. CONCLUSIONS: The findings suggest that, in assessment of complex performance in ill-defined situations, the usual practice of asking experts to reach a consensus on each item reduces and hinders the detection of expertise. If these results are confirmed by other researches, this practice should be reconsidered.
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.
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 | 0.019 | 0.168 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.004 |
| 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 it