483 Transforming Knee Replacement Surgery: A Global Analysis of Day Case Innovations from OECD Database
Bibliographic record
Abstract
Abstract Aim Day case surgery is transforming Knee Replacement Surgery by enhancing efficiency while maintaining clinical outcomes. This approach addresses growing surgical demand and backlogs, exacerbated by the COVID-19 pandemic. This study utilises the Organisation-for Economic-Co-operation-and-Development (OECD) global database to explore the adoption, implementation strategies, and impact of day case knee replacement surgery across member nations. Method A retrospective analysis of OECD health datasets, national healthcare reports, and institutional case studies was conducted. Data on knee replacement volumes, waiting times, and day case procedure rates from 2015 to 2023 were extracted. Temporal trends were stratified by country, and qualitative insights from case studies highlighted successful implementation strategies. Results Significant disparities in the adoption and delivery of day case knee replacement surgery were identified across OECD nations. Canada reported the highest cumulative volume, delivering over 15,000-day case procedures between 2015 and 2022. However, the proportion of day cases in Canada declined to 83% by 2022, compared to an average of 97% in other OECD nations. In contrast, the United Kingdom performed only 1,500 cumulative day case procedures during the same period, reflecting a slow adoption rate. Recovery efforts in the UK, including the establishment of elective hubs and adoption of Enhanced Recovery After Surgery (ERAS) protocols, have demonstrated early promise in addressing surgical backlogs. Conclusions Day case knee replacement surgery presents a viable solution to reduce surgical backlogs while maintaining safety and efficacy. Wider adoption of evidence-based protocols, such as ERAS, could bridge disparities in care delivery and improve outcomes globally.
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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 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.013 | 0.022 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".