Follow‐up evaluation of a web‐based pediatric brain tumor board in Latin America
Bibliographic record
Abstract
BACKGROUND: Since 2013, pediatric oncologists from Central and South America discuss neuro-oncology cases with experts from North America and Europe in a web-based "Latin American Tumor Board" (LATB). Here, we evaluate the feasibility of recommendations rendered by the Board. METHODS: An electronic questionnaire was distributed to physicians who had received recommendations between October 2017 and October 2018. Physicians were asked regarding the feasibility of each recommendation given during the LATB discussion. Baseline case characteristics of all presented cases were obtained from anonymized minutes. RESULTS: Of the 142 patients discussed, data on 103 patients from 15 countries were available, corresponding to 283 recommendations. Physicians followed 60% of diagnostic procedural recommendations and 69% of therapeutic recommendations. The most difficult recommendations to follow were genetic and molecular testing, pathology review, chemotherapy, surgery, and molecular targeted therapies. Histological diagnoses changed in eight of 18 cases in which a pathology review was undertaken. Fifty-four percent of the recommendations that could not be implemented were considered not feasible in the specific context of the patient, while 31% were not implemented due to a decision of the medical staff or the parents (15% not specified). However, 96% of respondents considered the recommendations useful. CONCLUSION: Recommendations were frequently perceived as useful, and were applicable in the participating institutions. Nevertheless, limitations in availability of diagnostic procedures and treatment modalities affected the feasibility of some recommendations. Tele-oncology tumor boards offer physicians from low- and middle-income countries access to real-time, high-level subspecialist expertise and provide a valuable platform for worldwide information exchange.
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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 | 0.014 | 0.057 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".