The completeness of national hip and knee replacement registers
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: National joint replacement registries were developed for prospective monitoring of outcomes and post-market surveillance of implants. Increasingly registry data informs practice. However, analysis of a registry can only be as good as the data it captures on the population of interest. We aimed to analyze completeness of reporting of hip and knee replacement procedures for all national registries worldwide. METHODS: We analyzed annual reports and data provided following written requests to all active national hip and knee replacement registries. Coverage was defined as the proportion of hospitals in the country that participate in the registry. Procedure completeness was defined as the proportion of procedures successfully captured by the registry. RESULTS: 14 national registries were included, spanning years 2004 to 2022. Coverage was complete in 10. Median procedure completeness for primary hip and knee replacement across all years was 96.5% (interquartile range [IQR] 94.0-97.7%). Median procedure completeness for revisions was 88.5% (IQR 81.0-92.5%). The terminology used and method of calculation of completeness estimates in the registries were variable. CONCLUSION: National hip and knee replacement registry data generally reflects excellent coverage (full in 10 of 14 registries) and completeness (primary procedures 96.5% and revisions 88.5%) over the last 2 decades.
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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 | 0.049 | 0.153 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".