Evaluating Treatment Plan Modifications from Surgeons’ Initial Recommendations to Multidisciplinary Tumor Board Consensus for Cancer Care in a Resource-Limited Setting
Bibliographic record
Abstract
Multidisciplinary tumor boards (MTBs) are essential for optimizing cancer care through collaborative decision-making. However, the concordance between initial surgeons’ recommendations and MTB outcomes, particularly in resource-limited settings, remains underexplored. This study evaluates the agreement between treatment plans proposed initially by surgeons and those finalized through MTB discussions conducted at the same stage of patient evaluation, with a focus on changes in treatment intent between curative and palliative care. A retrospective analysis of 216 patients discussed at bi-weekly MTB meetings between January 2021 and December 2023 at a tertiary care hospital was conducted. Statistical tests, including kappa statistics and concordance analysis were applied to assess the interrater agreement between surgeon-recommended and MTB-finalized decisions and to evaluate changes in treatment intent. A p-value < 0.05 was considered statistically significant. Strong concordance and significant perfect agreement were observed between curative versus palliative decisions of surgeons and MTBs, (Cohen’s kappa = 0.89, p < 0.001). MTB recommendations were added to the surgeons’ suggested plans in 38.4% (n = 83) of cases and replaced them entirely in 25.0% (n = 54) of cases. Shifts in treatment intent from curative to palliative or vice versa were infrequent (2.31%, n = 5), specifically in esophageal and stomach cancers. MTB decisions achieved a 100% implementation rate. This study underscores the critical role of MTBs in collaborative decision-making and their value as an essential tool for consistent, individualized, and evidence-based cancer care.
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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 | 0.076 | 0.196 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".