Virtual postoperative clinic: can we push virtual postoperative care further upstream?
Bibliographic record
Abstract
Virtual care refers to remote healthcare interactions between patients and health professionals, predominantly using telecommunications networks. Virtual care interactions are a form of information exchange that guides care decisions. These interactions aim to enhance the patient experience and outcomes of care. Healthcare-related virtual care interactions can range from video clinic appointments to remote monitoring.1 The role of virtual care in surgery is rapidly evolving, and the nature of virtual interactions varies according to the phase of the surgical journey—spanning preoperative evaluation and assessment, preparation for surgery, intraoperative care and postoperative care. The history of virtual surgical postoperative care actually goes back several decades. In a cohort of 536 patients with hip fracture published in 1990, telephone contact predicted return of function a year following surgery, likely due to improving patients’ psychological function, reinforcing postoperative medication regimens and encouraging consistent participation in rehabilitation.2 Despite a rapidly evolving technological landscape, telephone contact with patients remains a cornerstone of remote interactions with patients after surgery. In this issue of BMJ Quality & Safety , Healy et al 3 report the results of a randomised controlled trial comparing a telephone-based virtual outpatient clinic with an actual outpatient clinic for the follow-up of general surgery patients 6–8 weeks after discharge from hospital. Of 107 subjects randomised to virtual follow-up, 98 (92%) were successfully contacted by telephone, of which 10 (10%) had postoperative issues and 3 of whom …
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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 | 0.008 | 0.050 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.005 | 0.012 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.032 | 0.023 |
| Insufficient payload (model declined to judge) | 0.018 | 0.006 |
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".