LMIC-25. EXPANDING THE PEDIATRIC NEURO-ONCOLOGY TELECONFERENCE EXPERIENCES, A LUXURY, OR A NECESSITY?
Bibliographic record
Abstract
Abstract INTRODUCTION Videoteleconference in neurooncology is feasible and sustainable. The well-established 20 years’ Sickkids/KHCC teleconferencing is an example. Since 2018, several regional centers joined these meetings to discuss their patients’ management plans. We aimed to evaluate the impact of this experience. METHODS We retrospectively reviewed the 56 meetings’ minutes (9/2018-12/2023) and compared the pre-conference suggested plans with the post-conference recommendations. We recorded if the recommendations were implemented and the impact perceived. RESULTS In total, 251 patients were discussed: KHCC (137) and non-KHCC (114). Four of the regional participating oncologists had dedicated pediatric neuro-oncology training. Patients were selected for discussion due to challenges in their management (i.e. with relapsed tumors, rare diagnoses, unexpected tumor behavior). Main diagnoses were: Low-grade-glioma (28%), High-grade-glioma (21%), and Medulloblastoma (17%). Of the 227 patients where the local team suggested a care plan, the teleconference recommendations concurred with the proposed plan in 50% of the cases, agreed on but provided an alternative plan option in 18% and disagreed on in 32% of the cases. The difference in recommendations mostly affected the proposed treatment modality (15%). In 64% of the discordant plans and 50% of the alternative plans, the treating team applied the recommendations. Challenges to apply the recommendations were mainly linked to patients’ factors (53%, plan refusal, traveling abroad, clinical deterioration), local multidisciplinary team consensus on a different plan (29%) or logistic difficulties (18%, e.g. drug access). CONCLUSIONS Improvement in patients’ care was likely achieved with the continuous effort of the local teams to implement the recommendations. Participating oncologists valued the shared educational experience learned from each other’s case discussions, especially those related to molecular-testing and implications on treatment. Joining such regional teleconferences is of particular importance to small-volume centers or those lacking a pediatric neurooncologist. Regional and international collaborations are a necessity when it comes to improving the care of children with CNS tumors.
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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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 0.003 |
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".