Implementation of a student-run initiative to facilitate multidisciplinary cancer care in Pakistan: the Tumor Board Establishment Facilitation Forum
Bibliographic record
Abstract
BACKGROUND: The lack of a multidisciplinary approach to the management of cancer patients in most parts of Pakistan is a long-standing and major concern. To overcome this disparity, we started a student-run initiative to facilitate the establishment of multidisciplinary tumor boards (MTBs) in oncology, titled "Tumor Board Establishment Facilitation Forum (TEFF)". The objectives of this study were to evaluate the clinical and academic impact of TEFF on cancer care for patients and student education, respectively. METHODS: The formation of TEFF was based on the Theory of Change model. We conducted a needs assessment based on existing literature, physical evaluation of wards, and consultation with senior academic faculty members. The logic model was refined through multiple meetings between stakeholders. All engagements of TEFF described in this manuscript are limited to the Dow Medical College and its affiliated tertiary care hospital, Dr. Ruth K. M. Pfau Civil Hospital in Karachi, Pakistan. To gauge TEFF's impact, we used administrative data generated between October 2021 to March 2024 to evaluate predefined outcomes (number of MTBs, cases, educational interventions, and research). RESULTS: The organizational structure comprised of 6 specialized departments: Communications, Operations, Media, Integrated Development, Research and Records, and Finance. We conducted 18 educational sessions for medical students about career guidance, research, cancer awareness; and 4 cancer awareness campaigns. TEFF facilitated the formation of 4 MTBs: breast, head and neck, gynecology, and pediatrics. Across these, 105 cases were discussed in 50 meetings. TEFF provided leadership opportunities, allowed familiarization with oncology, raised awareness of challenges associated with cancer care, allowed networking, and inculcated research-related skills and educational value through its MTBs. CONCLUSION: Medical students can contribute significantly to clinical care at the undergraduate level. For countries/regions struggling to provide multidisciplinary cancer care, TEFF's model serves as a blueprint for a viable solution.
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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 | 0.012 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".