DEV-14. IMPACT OF A LATIN AMERICA-WIDE TELECONFERENCED BRAIN TUMOR BOARD
Bibliographic record
Abstract
Pediatric-cancer cure rates are ~80% in high-income countries (HIC) however, 80% of children with cancer live in low-to-middle income countries (LMIC) where chances for cure are much lower. Pediatric neuro-oncology requires a multi-disciplinary effort challenging to achieve in LMIC. We started a pediatric neuro-oncology teleconference to help improve healthcare disparities for children in Latin America (LA) with brain tumors, herein we summarize our efforts. Utilizing a web-portal provided by cure4kids.org we connect HIC-global-pediatric neuro-oncologists with LA pediatric-subspecialists. Weekly, real-time, 60-minute sessions are held in Spanish. Minutes are sent in Spanish after each meeting to solidify recommendations and education. In 2013, we started with one country and 6 members. Substantial growth and data acquisition occurred to include 20-countries throughout LA with growing membership, 2015: 44, 2016: 121, and 2017: 199; and participation, median of 17 (range: 9–22) in 2016 to 25 (range: 11–40) in 2017 (p=0.0005). This required more sessions 2015: 13, 2016: 34, 2017: 47. The total number of cases reviewed were, 2015: 22, 2016: 73, 2017: 132; demonstrating 2017 had statistically more cases per session, on average, than 2016 (p=0.0049) and 2015 (p=0.0075). The growing size of our program demonstrates a reassuring response by LA. We provide systematic and effective communication among LA providers in their language. We succeeded in connecting LA institutions of differing resources and experience with global-pediatric neuro-oncologists from leading cancer-centers in HIC to help improve the care of children with brain tumors in LA and promote continuing education for their providers.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".