A systematic review of validated methods for identifying orthopedic implant removal and revision using administrative data
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.074 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it