LINC-18. FOLLOW-UP EVALUATION OF A WEB-BASED PEDIATRIC BRAIN TUMOR BOARD IN LATIN AMERICA
Bibliographic record
Abstract
Abstract BACKGROUND Since 2013, pediatric oncologists from Latin America have discussed neuro-oncology cases with experts from North America and Europe in a web-based “Latin American Tumor Board” (LATB). This descriptive study evaluates the feasibility of the recommendations rendered during the Board. METHODS An electronic questionnaire was distributed to physicians who received recommendations between October 2017 and October 2018, two months after their case presentation on the LATB. Physicians were asked regarding the feasibility of each recommendation given during the Board. Baseline case characteristics of all presented cases were obtained from anonymized minutes prepared after the presentations. RESULTS 36 physicians from 15 countries answered 103 of 142 questionnaires (72.5%), containing 283 recommendations. Physicians followed 60% of diagnostic procedural recommendations and 70% of therapeutic recommendations. Overall, 96% of respondents considered the recommendations applicable and useful. The most difficult recommendations to follow were genetic and molecular testing, pathology review, locally adapted chemotherapy protocols administration, neurosurgical interventions and access to molecular targeted therapies. The most cited reasons for not implementing the recommendations were lack of resources, inapplicable recommendations to that low-to-middle income country (LMIC) setting, and lack of parental consent. CONCLUSION The recommendations given on the LATB are frequently applicable and helpful for physicians in LMIC. Nevertheless, limitations in availability of both diagnostic procedures and treatment modalities affected the feasibility of some recommendations. Virtual tumor boards offer physicians from LMIC access to real time, high-level subspecialist expertise and provide a valuable platform for information exchange among physicians worldwide.
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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 | 0.011 | 0.044 |
| 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 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".