Effect of Multidisciplinary Case Conferences on Physician Decision Making: Breast Diagnostic Rounds
Bibliographic record
Abstract
PURPOSE: To evaluate the utility of multidisciplinary case conferences (MCCs) on physician decision making in benign and malignant breast disease management. METHODS: Patients with interesting or challenging diagnostic or management issues were discussed at biweekly diagnostic breast MCCs. Prior to discussion, a clinical summary and intended management plan prior to the MCC was presented. For each case, diagnostic images/histopathology were centrally reviewed after which group discussion achieved a management consensus which was documented prospectively. Initial management plans were compared to the post-MCC consensus. A change in a management plan was defined as a consensus plan different from the pre-MCC plan or no definite plan prior to the MCC. RESULTS: From November 2014 to December 2015, 76 patients (43 malignant and 33 benign diagnoses) were discussed in 19 MCCs. All cases presented resulted in a consensus management recommendation. Thirty-one case discussions (41%) resulted in a changed management plan (20 malignant and 11 benign diagnoses). Management changes included avoidance of immediate surgery (9% of cases), change in the type of surgery (5%), non-invasive investigation to invasive/surgical intervention (7%), and detection of a new suspicious lesion (1%). CONCLUSION: MCCs had a substantial impact on physician decision making. Management plans changed in 41% of cases presented, the majority due to new/clarified diagnostic information. Presentation of cases at MCCs should be encouraged, especially for challenging diagnostic or management issues regarding malignant or benign breast diagnoses.
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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 | 0.026 | 0.145 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".