Multidisciplinary approach to cancer care in Rwanda: the role of tumour board meetings.
Bibliographic record
Abstract
Introduction: Cancer treatment is complex and necessitates a multidisciplinary approach. Tumour Board Meetings (TBMs) provide a multidisciplinary platform for health care providers to communicate about treatment plans for patients. TBMs improve patient care, treatment outcomes and, ultimately, patient satisfaction by facilitating information exchange and regular communication among all parties involved in a patient's treatment. This study describes the current status of case conference meetings in Rwanda including their structure, process and outcomes. Methods: The study included four hospitals providing cancer care in Rwanda. Data gathered included patients' diagnosis, number of attendance and pre-TBM treatment plan, as well as changes made during TBMs, including diagnostic and management plan changes. Results: From 128 meetings that took place at the time of the study, Rwanda Military Hospital hosted 45 (35%) meetings, King Faisal Hospital had 32 (25%), Butare University Teaching Hospital (CHUB) had 32 (25%) and Kigali University Teaching Hospital (CHUK) had 19 (15%). In all hospitals, General Surgery 69 (29%) was the leading speciality in presenting cases. The top three most presented disease site were head and neck 58 (24%), gastrointestinal 28 (16%) and cervix 28 (12%). Most (85% (202/239)) presented cases sought inputs from TBMs on management plan. On average, two oncologists, two general surgeons, one pathologist and one radiologist attended each meeting. Conclusion: TBMs in Rwanda are increasingly getting recognised by clinicians. To influence the quality of cancer care provided to Rwandans, it is crucial to build on this enthusiasm and enhance TBMs conduct and efficiency.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".