SURGICAL PRE-ASSESSMENT OF ELECTIVE ORTHOPAEDIC CONDITIONS USING REMOTE VIDEO-CONFERENCING: PROSPECTIVE STUDY
Bibliographic record
Abstract
Background- Health care is best delivered face to face, doctor to patient. However, in some places like Scotland, patients can be in remote areas, far from the nearest health care provider. Medical video conferencing (VC) enables patients and doctors to meet for consultations from wherever they may be without the need for travel, and is already used widely in countries like Australia and Canada. Aim- To do a pilot study of using the existing VC facility at our hospital for surgical pre-assessment of patients for elective foot/ankle and lower limb arthroplasty surgery. Methods- A prospective pilot study was performed at our hospital after approval from our ethics committee. Patient-records were vetted to include/exclude from the study and cases considered as “straightforward” were included. Two separate rooms with VC facility were set up in the orthopaedic outpatients, one with the patient and a trained physiotherapist, while the surgeon used the second room to discuss patient9s complaints, do a physical examination, and discuss surgery where appropriate. Results- 120 patients were included in the study out of which 82 (68 %) patients were seen exclusively through VC. Out of the 82, there were 42 foot/ankle cases and 40 cases for lower limb arthroplasty (TKR/THR). There were 40 cases where the VC consultation had to be converted into a face-to-face consultation mainly because the surgeon (31/40) was not entirely satisfied with the VC consultation (inadequate information in notes, anxious patient, etc). Conclusion- We found that the VC consultation worked for a large majority of “straightforward” elective foot/ankle and lower limb arthroplasty conditions. We believe that there is certainly a role for VC consultation, especially for patients in rural/remote areas of Scotland, thus bringing down the cost and inconvenience of patient transport considerably.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".