Impact of telemedicine on pediatric neuro‐oncology in a developing country: The Jordanian‐Canadian experience
Bibliographic record
Abstract
BACKGROUND: Telemedicine is widely used in industrialized countries for educational purposes. Twinning experiences using telemedicine between institutions in industrialized and developing countries (DC) have been limited. Pediatric neuro-oncology is a complex multidisciplinary discipline that is underserved in most of DC and provides a model to test the feasibility of such tool for twinning purposes. METHODS: A computer, an EMLO visual presenter HV-7600SX document camera, and a TANDBERG 6000 model videoconference unit were used to present data. For connectivity, we used a six-channel ISDN telephone line. Each channel is 64 megabytes/sec. RESULTS: Between December 2004 and May 2006, 20 sessions of videoconference were held between King Hussein Cancer Center and the Hospital for Sick Children to discuss 72 cases of 64 patients with various brain tumors (5 patients were discussed twice and 1 patient four times). In 23 patients (36%), major changes from original plan were recommended on different aspects of the care. In 21 patients (91%), those recommendations were followed, with potentially significant positive impact on patients' care. CONCLUSIONS: Videoconferencing is a feasible and practical twinning tool in pediatric neuro-oncology with a potentially major impact on patient care.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".