308. Identification of Prosthetic Hip and Knee Joint Infections in Administrative Databases
Bibliographic record
Abstract
Canada lacks a prosthetic hip and knee joint infection (PJI) registry, leaving active surveillance to be orchestrated by individual hospitals, which is limited by cost and narrow scope. Administrative databases are potentially an ideal instrument for infection surveillance, but detection algorithms relying solely on PJI diagnostic codes alone have been hampered by low specificity. There is a need to develop improved strategies to efficiently and accurately identify PJIs using health administrative databases. Combinations of International Classification of Disease, Tenth Revision, diagnostic and procedure codes were used to create testing cohorts among individuals treated at two institutions in Toronto, Ontario, from April 1, 2015 until March 31, 2016. These cohorts were compared with a reference standard of PJIs, which were identified by chart reviews of every individual who underwent a hip or knee revision operation at these institutions during the study period. The primary outcomes were the performance characteristics of each algorithm. Over the 1-year study period, there were 471 revision operations for 405 patients, of which 155 (33%) were performed for the treatment of a PJI. Of the 405 individuals, 108 (27%) had a PJI as the surgical indication; there were 57 (53%) two-stage procedures, nine (8%) single-stage procedures, 34 (31%) incision and drainage procedures with implant retention, and eight (7%) excisional arthroplasties. The combination of a revision operation code plus a PJI diagnosis code was the most robust detection method: sensitivity 0.86 (95% confidence interval, 0.79–0.91) and specificity 0.99 (0.98–1.00). Coupling codes for a revision operation and insertion of a peripherally inserted central catheter yielded a sensitivity of 0.45 (0.37–0.53) and specificity of 1.00 (0.98–1.00). PJI codes alone had a sensitivity of 1.00 (0.86–1.00) and specificity 0.50 (0.23–0.77). The combination of a revision operation procedure code and a PJI diagnosis code is sensitive and specific for the detection of a PJI in administrative databases. This is a promising avenue for national PJI surveillance and has the potential to facilitate future research in the prevention and management of PJIs. All authors: No reported disclosures.
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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 | 0.007 | 0.050 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".