LINC-11. NEUROPATHOLOGY REVIEW OF LATIN AMERICAN CHILDHOOD AND ADOLESCENT BRAIN TUMOR PATIENTS: A MULTI-NATIONAL, MULTI-DISCIPLINARY PEDIATRIC NEURO-ONCOLOGY TELECONFERENCE EXPERIENCE
Bibliographic record
Abstract
Abstract BACKGROUND Pediatric brain tumor classification has undergone significant evolution over the last decade requiring a high-level of expertise and diagnostic techniques. Such advances have created challenges for pathologists particularly in low-to-middle income countries (LMIC). We conduct weekly pediatric neuro-oncology teleconferences linking global pediatric neuro-oncologists from high-income countries (HIC) to review patients with pediatric subspecialists from Latin America. METHODS Three to five patients are discussed weekly and second neuropathology review is offered when a high-level of suspicion emerges of a questionable diagnosis based on clinical and radiographical information. Nationwide Children’s Hospital (NCH) provides second neuropathology review at no cost to institutions in Latin America that fulfill these criteria. RESULTS From July 2015 to December 2019 NCH reviewed 54 pathology samples from eleven Latin American countries. Of these, 33 (61.1%) cases resulted in diagnostic changes, of which 28 (51.8%) were significant, impacting treatment plans and overall patient outcomes. The remaining 21 (38.9%) confirmed institutional diagnosis; however, in eight of these 21 cases additional molecular information and/or further tumor subtyping unavailable in their home country at the time (eg: BRAF, RELA-fusion, medulloblastoma subtyping) was provided. CONCLUSIONS This study highlights the importance of centralized pathology review by institutions with the proper equipment, infrastructure and expertise in pediatric neuropathology. Furthermore, this documents the beneficial impact of teleconferencing for subspecialists in LMIC who must treat a wide variety of pediatric cancers with few resources and support. Additionally, our findings underscore the need for pediatric subspecialty training in LMIC.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.003 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".