A systematic review of validated methods for identifying orthopedic implant removal and revision using administrative data
Bibliographic record
Abstract
PURPOSE: To identify studies that have validated administrative and claims database algorithms for identifying patients with orthopedic device revision or removal. METHODS: As a part of the Food and Drug Administration's Mini-Sentinel pilot program, we performed a systematic review to identify algorithms for orthopedic implant removal/revision in administrative and claims databases in the USA or Canada. RESULTS: Five studies examined the validity of database algorithms against a gold standard of documentation in medical records (n = 3) or codes/documentation in another database (n = 2). The positive predictive values (PPV) of the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) and/or the Current Procedural Terminology codes for revision total hip arthroplasty (THA) in the US Medicare population compared with medical record review were 92%and 91%, respectively. In another study of the US Medicare population, multiple ICD-9 codes for revision total knee arthroplasty were compared with newly available single ICD-9-CM codes for revision knee arthroplasty; sensitivity was 87% and specificity was 99% (PPV not provided). The fourth study validated the ICD-9-CM codes for revision total knee arthroplasty against Ontario health insurance physician fee service claims as the gold standard and found a PPV of 32%. In the last study in Medicare population, the accuracy of the attribution of revision THA to the same side as the earlier index primary THA was examined; PPV for same laterality of revision THA was 71% (using ICD-9-CM codes). CONCLUSIONS: Validation data, with regard to the ICD-9-CM or the Current Procedural Terminology code algorithms for revision THA in the Medicare population, exist. More validation studies are needed to confirm these findings and examine other large databases.
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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 | 0.039 | 0.234 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.010 |
| Bibliometrics | 0.023 | 0.019 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 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".