The Impact of the Pathologist in Multidisciplinary Cancer Conferences on Patient Care : Evidence From the Literature
Bibliographic record
Abstract
OBJECTIVES: Multidisciplinary cancer conferences (MCCs) are important tools in the treatment of patients with complex health issues, helping clinicians achieve optimal outcomes in oncological practice. To explore the role of pathologists at MCCs, we conducted a review of prior research on this topic. METHODS: We conducted a scoping review by searching MEDLINE, EMBASE, and the Cochrane Library for English-language qualitative, quantitative, or multiple/mixed methods studies on the role and impact of pathologists on MCCs. We used Microsoft Excel to extract data. RESULTS: Of 76 research results, we included only 3 studies that involved review of cancer cases by pathologists for MCCs. All 3 studies showed that expert pathology review improved the accuracy of diagnosis and refined disease staging, leading to changes in the management of melanoma, breast cancer, and gynecologic cancer. No studies explored the barriers to pathologists participating in MCCs or the strategies or interventions employed to promote or support pathologist involvement. CONCLUSIONS: We identified a paucity of studies on the role of pathologists in MCCs. Given the positive impact of MCCs involving pathologists on the accuracy of diagnosis and optimization of treatment, future research is warranted to further establish the role and impact of pathologists in MCCs and how to promote or support pathologists' involvement.
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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 | 0.018 | 0.100 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".