Bibliographic record
Abstract
Telemedicine and rural care Telemedicine can be a marvellousbridge between rural hospitals and tertiary centres, but the spectacu-lar case recounted by Bruce Campana and colleagues1 may not illustrate this point well. Telemedicine works best when it does not degrade local care. The article seems to imply that the rural hospitals involved would deny patients proper care without the video presence of an urban specialist. This implication insults the staff of rural hospitals without access to telemedi-cine, who competently handle a vari-ety of serious conditions with out-comes equal to or better than those achieved by their tertiary care coun-terparts. Drilling a burr hole, although a rare procedure, is not a skill requiring ad-vanced neurosurgical expertise. Rural doctors have sometimes had to do craniotomies themselves, the skill be-ing swiftly acquired.2 Perhaps the local hospital described by Campana and colleagues lacked training and equip-ment, given that many rural facilities are becoming triage centres that also offer geriatric and palliative care. More likely, however, the telemedi-cine — while providing a measure of reassurance for what is probably a su-perb rural hospital — reinforced the authors ’ notion that burr holes, trauma or any advanced care cannot be han-dled competently without an urban specialist. No one knows the financial cost of making telemedicine widely available in rural Canada, but enhancing local skills and equipment (through provi-sion of CT scanners and operating rooms along with well-trained gener-alists) could probably be achieved at a fraction of that expense. The latter op-tion would improve morale and out-comes more than images on a video screen. Telemedicine could then be used in a more selective, effective manner than the authors ’ “protean” hopes.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.417 | 0.237 |
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".