A mixed-method review of the efficacy of a virtual fracture clinic: an 8-year follow up
Bibliographic record
Abstract
Background: Virtual fracture clinics are well established methods of managing trauma patients safely and effectively. While they have been widely adopted, there is a lack of research on their longer-term implementation and success. Aims: This paper aimed to review factors that may influence the success of a virtual fracture clinic. Methods: Business analytics were used to calculate clinic discharge and return rates, as well as the reasons for them. Qualitative analysis was used to look at patient satisfaction with virtual consultations; data were extracted from two differing time points via telephone interviews. Results: The mean discharge rate across the 2014–2021 period was 28%. The largest discharge rate was seen in 2014 (43%) and the lowest was seen in 2021 (28%). Return rates to the emergency department at the two different time points were 2% and 0.9% respectively. Patient satisfaction was rated as ‘good’ or higher in all parameters at both time points. Discussion: This article describes the long-term success of a virtual fracture clinic. Results demonstrate excellent patient satisfaction coupled with the lowest return rates in the published literature. Discharge rates started extremely high and have declined with time. The factors for these results are discussed along with ideas for further prospective research.
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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 | 0.032 | 0.088 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".