LMIC-09. CRADLE TO CRAYON: THE IMPACT OF COLLABORATION ON PEDIATRIC CENTRAL NERVOUS SYSTEM TUMORS IN GHANA
Bibliographic record
Abstract
Abstract BACKGROUND A multidisciplinary team (MDT) approach is essential for quality care, especially in paediatric oncology. In developing countries like Ghana, paediatric neuro-oncology is a relatively young subspecialty and requires a multidisciplinary team approach. The St Jude Global Academy Neuro-Oncology Training Seminar in 2022 saw a significant milestone in this MDT journey. Four MDT members benefited from the training, with a profound impact. This study aims to study the effect of neuro-oncology training on the number of CNS cases reviewed at MDT meetings. METHODS A retrospective review of case announcements made on the Pediatric Oncology MDT WhatsApp group page were collated spanning the period August 1, 2022, to July 31, 2023. Tumour distribution of cases discussed at MDT was compared 4 months pre- and 4- and 8-months post-St Jude Global Academy Neuro-Oncology Training Seminar (NOTS) completion in November 2022. A T-test was used to calculate for statistical significance of the difference in the number of cases discussed over the period. RESULTS A total of 208 cases were discussed after 52 MDT Zoom meetings. The number of cases discussed at MDT increased from 40 recorded in the first four months (August -November 2022) to 89 cases in the last four months (April- July 2023). There was a significant increase in the number of CNS cases discussed by 3.9% (p=0.012) at 4 months and 6.6% (p=0.023) at 8 months after NOTS. The complexity of CNS tumours discussed also increased. CONCLUSIONS The St Jude Global NOTS positively influenced the number of CNS cases discussed at MDT. Multidisciplinary team discussions allow for collations of ideas from all specialists with a patient-centred approach which is particularly invaluable to the care of neuro-oncology patients. Further studies on the effect on time of plan implementation, and client satisfaction will be beneficial to evaluate the effectiveness of our MDTs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".