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Record W1997652809 · doi:10.1002/pbc.21085

Impact of telemedicine on pediatric neuro‐oncology in a developing country: The Jordanian‐Canadian experience

2006· article· en· W1997652809 on OpenAlexaffabout
Ibrahim Qaddoumi, Asem Mansour, Awni Musharbash, James M. Drake, Maisa Swaidan, Tarık Tihan, Éric Bouffet

Bibliographic record

VenuePediatric Blood & Cancer · 2006
Typearticle
Languageen
FieldMedicine
TopicTelemedicine and Telehealth Implementation
Canadian institutionsHospital for Sick Children
Fundersnot available
KeywordsMedicineTelemedicinePediatric oncologyVideoconferencingMultidisciplinary approachDeveloping countryDeveloped countryFamily medicineMedical emergencyCancerPediatricsHealth careInternal medicineMultimedia

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.142
Threshold uncertainty score0.943

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.022
GPT teacher head0.359
Teacher spread0.337 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations98
Published2006
Admission routes2
Has abstractyes

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