Evaluation of a telemedicine demonstration project in the Magdalene Islands
Bibliographic record
Abstract
The Magdalene Islands are an archipelago located in the middle of the Gulf of St Lawrence, more than 1000 km away from supra-regional medical referral centres. We have implemented and evaluated a telemedicine network for the local hospital on the Magdalene Islands. During a 13-month study period, 118 transmissions were made. Orthopaedics and radiology were the medical specialties that used telemedicine most frequently. Store-and-forward imaging was the technique used most often because of the large number of transmissions in orthopaedics and radiology. Various medical specialties and psychosocial services used videoconferencing, while realtime imaging (ultrasound) was used in gynaecology and obstetrics. A combination of videoconferencing and imaging was used for otolaryngology. A total of 101 individual patients benefited from a teleconsultation during the study period. Eight emergency transfers were avoided and 15 patients who would have required elective transfer were managed locally by telemedicine. For health-care providers, telemedicine seemed to be an acceptable way of delivering specialized services. Nevertheless, demonstration projects in telemedicine are quite different to 'real life' telemedicine utilization. Deployment of telemedicine in the health-care system as a whole will require a more structured approach.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".