Medical and Nonmedical Information during Multidisciplinary Team Meetings in Cancer Care
Bibliographic record
Abstract
BACKGROUND: Multidisciplinary team (MDT) meetings provide treatment recommendations based on available information and collective decision-making in teams with complementary professions, disciplines and skills. We aimed to map ancillary medical and nonmedical patient information during case presentations and case discussions in MDT meetings in cancer care. METHODS: Through a nonparticipant, observational approach, we mapped verbal information on medical, nonmedical and patient-related characteristics and classified these based on content. Data were collected from 336 case discussions in three MDTs for neuro-oncology, sarcoma and hepato-biliary cancer. RESULTS: Information on physical status was presented in 48.2% of the case discussions, psychological status in 8.9% and comorbidity in 48.5% of the cases. Nonmedical factors, such as family relations, occupation, country of origin and abode were referred to in 3.6-7.7% of the cases, and patient preferences were reported in 4.2%. CONCLUSIONS: Provision of information on comorbidities in half of the cases and on patient characteristics and treatment preferences in <10% of case discussions suggest a need to define data elements and develop reporting standards to support robust MDT decision-making.
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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 | 0.013 | 0.076 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".