The legal and socioeconomic considerations of spine telemedicine in Canada
Bibliographic record
Abstract
Telemedicine, or virtual care offers a platform for remote assessments, for either initial consultations or follow-up care. Telemedicine is a broad term and may refer to video conferences/assessments, telephone visits, messages through online platforms, and remote monitoring applications. The restrictions during the coronavirus disease 2019 (COVID-19) crisis had accelerated the use of telemedicine in Canadian healthcare. Several years after the pandemic, after this initial trial of widespread telemedicine, there remains significant uncertainty as to its efficacy and future directions. There are inherent challenges to telemedicine, including questions of clinical reliability and privacy, balanced against the possibility of efficiency and increased access to specialists. The Canadian healthcare system also poses significant challenges in the evaluation and systemic implementation of telemedicine, given the lack of a national legal framework and separate provincial or territorial regulation systems across the country. Telemedicine is of a particular interest to spinal surgeons, given the prevalence, morbidity, and economic costs associated with spinal pathologies. Prior to the COVID-19 pandemic, few spine surgeons offered telemedicine, due to the perceived challenges of remote assessment and diagnosis with spine pathologies. There has been little subsequent data to examine the role and suitability for remote acre in spine surgery. Herein, we review the current landscape of telemedicine in Canadian healthcare, with applications to spine surgery.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".