Crowdsourcing a diagnosis? Exploring the accuracy of the size and type of group diagnosis: an experimental study
Bibliographic record
Abstract
BACKGROUND: The consultation process, where a clinician seeks an opinion from another clinician, is foundational in medicine. However, the effectiveness of group diagnosis has not been studied. OBJECTIVE: To compare individual diagnosis to group diagnosis on two dimensions: group size (n=3 or 6) and group process (interactive or artificial groups). METHODOLOGY: Thirty-six internal or emergency medicine residents participated in the study. Initially, each resident worked through four written cases on their own, providing a primary diagnosis and a differential diagnosis. Next, participants formed into groups of three. Using a videoconferencing platform, they worked through four additional cases, collectively providing a single primary diagnosis and differential diagnosis. The process was repeated using a group of six with four new cases. Cases were all counterbalanced. Retrospectively, nominal (ie, artificial) groups were formed by aggregating individual participant data into subgroups of three and six and analytically computing scores. Presence of the correct diagnosis as primary diagnosis or included in the differential diagnosis, as well as the number of diagnoses mentioned, was calculated for all conditions. Means were compared using analysis of variance. RESULTS: For both authentic and nominal groups, the diagnostic accuracy of group diagnosis was superior to individual for both the primary diagnosis and differential diagnosis. However, there was no improvement in diagnostic accuracy when comparing a group of three to a group of six. Interactive and nominal groups were equivalent; however, this may be an artefact of the method used to combine data. CONCLUSIONS: Group diagnosis improves diagnostic accuracy. However, a larger group is not necessarily superior to a smaller group. In this study, interactive group discussion does not result in improved diagnostic accuracy.
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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 | 0.003 | 0.055 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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".