The ORthopaedic Trauma Hospital Outcomes - Patient Operative Delays (ORTHOPOD) study
Bibliographic record
Abstract
Aims: This is a multicentre, prospective assessment of a proportion of the overall orthopaedic trauma caseload of the UK. It investigates theatre capacity, cancellations, and time to surgery in a group of hospitals that is representative of the wider population. It identifies barriers to effective practice and will inform system improvements. Methods: Data capture was by collaborative approach. Patients undergoing procedures from 22 August 2022 and operated on before 31 October 2022 were included. Arm one captured weekly caseload and theatre capacity. Arm two concerned patient and injury demographics, and time to surgery for specific injury groups. Results: Data was available from 90 hospitals across 86 data access groups (70 in England, two in Wales, ten in Scotland, and four in Northern Ireland). After exclusions, 709 weeks' of data on theatre capacity and 23,138 operations were analyzed. The average number of cases per operating session was 1.73. Only 5.8% of all theatre sessions were dedicated day surgery sessions, despite 29% of general trauma patients being eligible for such pathways. In addition, 12.3% of patients experienced at least one cancellation. Delays to surgery were longest in Northern Ireland and shortest in England and Scotland. There was marked variance across all fracture types. Open fractures and fragility hip fractures, influenced by guidelines and performance renumeration, had short waits, and varied least. In all, nine hospitals had 40 or more patients waiting for surgery every week, while seven had less than five. Conclusion: There is great variability in operative demand and list provision seen in this study of 90 UK hospitals. There is marked variation in nearly all injuries apart from those associated with performance monitoring. There is no evidence of local network level coordination of care for orthopaedic trauma patients. Day case operating and pathways of care are underused and are an important area for service improvement.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".