Team Approach: Virtual Care in the Management of Orthopaedic Patients
Bibliographic record
Abstract
»: Telemedicine and remote care administered through technology are among the fastest growing sectors in health care. The utilization and implementation of virtual-care technologies have further been accelerated with the recent COVID-19 pandemic. »: Remote, technology-based patient care is not a "one-size-fits-all" solution for all medical and surgical conditions, as each condition presents unique hurdles, and no true consensus exists regarding the efficacy of telemedicine across surgical fields. »: When implementing virtual care in orthopaedics, as with standard in-person care, it is important to have a well-defined team structure with a deliberate team selection process. As always, a team with a shared vision for the care they provide as well as a supportive and incentivized environment are integral for the success of the virtual-care mechanism. »: Future studies should assess the impact of primarily virtual, integrated, and multidisciplinary team-based approaches and systems of care on patient outcomes, health-care expenditure, and patient satisfaction in the orthopaedic population.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".