Multidisciplinary Management of Cancer Patients: Chasing a Shadow or Real Value? An Overview of the Literature
Bibliographic record
Abstract
PURPOSE: Multidisciplinary cancer conferences (mccs) are designed to optimize patient outcomes. It appears intuitive that mccs are essential to clinical decision-making and patient management; however, it is unclear whether that belief is supported by evidence. Our objectives were to assess the currently published literature addressing the impact of mccs on clinical decision-making and patient outcomes. METHODS: Ovid medline was searched from 1950 to June 2010 using these keywords: "multidisciplinary/interdisciplinary/clinical meeting$/conference$/round$/team$," "decision making," "neoplasms$/cancer$/oncology/tumo(u)r conference$/board$/meeting$," "multidisciplinary/interdisciplinary cancer conference$/meeting$." All trials, guidelines, metaanalyses, reviews, and prospective and retrospective studies were included. RESULTS: The keywords retrieved 595 abstracts, and 30 manuscripts were obtained. Most of the studies assessed the impact of mccs on clinical decision-making rather than on patient outcomes. CONCLUSIONS: Available evidence supports the belief that mccs significantly influence clinical decision-making and treatment recommendations. In contrast, scant evidence suggests that mccs improve patient outcomes. Unfortunately, the current literature is substantially heterogeneous and therefore does not allow for firm conclusions.
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.
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 | 0.012 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.010 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 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".