Video-Teleconferencing in Pediatric Neuro-Oncology: Ten Years of Experience
Bibliographic record
Abstract
PURPOSE: The management of central nervous system tumors is challenging in low- and middle-income countries. Little is known about applicability of twinning initiatives with high-income countries in neuro-oncology. In 2004, a monthly neuro-oncology video-teleconference program was started between King Hussein Cancer Center (Amman, Jordan) and the Hospital for Sick Children (Toronto, Ontario, Canada). More than 100 conferences were held and > 400 cases were discussed. The aim of this work was to assess the sustainability of such an initiative and the evolution of the impact over time. METHODS: We divided the duration in to three eras according to the initial 2 to 3 years of work of three consecutive oncologists in charge of the neuro-oncology program at King Hussein Cancer Center. We retrospectively reviewed the written minutes and compared the preconference suggested plans with the postconference recommendations. Impact of changes on the patient care was recorded. RESULTS: Thirty-three sets of written minutes (covering 161 cases) in the middle era and 32 sets of written minutes (covering 122 cases) in the last era were compared with the initial experience (20 meetings, 72 cases). Running costs of these conferences has dropped from $360/h to < $40/h. Important concepts were introduced, such as multidisciplinary teamwork, second-look surgery, and early referral. Suggestions for plan changes have decreased from 44% to 30% and 24% in the respective consecutive eras. Most recommendations involved alternative intervention modalities or pathology review. Most of these recommendations were followed. CONCLUSION: Video-teleconferencing in neuro-oncology is feasible and sustainable. With time, team experience is built while the percentage and the type of treatment modifications change. Commitment and motivation helped maintain this initiative rather than availability of financial resources. Improvement in patients' care was achieved, in particular, with the implementation of a multidisciplinary team and the continuous effort to implement recommendations.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".