Effect of intra-articular hyaluronic acid agents on subsequent rate of infection following total knee arthroplasty
Bibliographic record
Abstract
The aim of our study was to discover any relation between infection, including deep infections, after knee total joint arthroplasty in patients who had previous intra-articular hyaluronic acid injections compared to those who had not. We performed a retrospective review of 1776 patients who had osteoarthritis and received hyaluronic acid injections and then a subsample of 415 patients who subsequently underwent total joint arthroplasty: a large primary researchable database from 2002-2008. Surgery was conducted in a university academic network, while the hyaluronic acid injections were delivered in a large primary care arthritis referral center. The 415 patients had at least one year follow-up after total joint arthroplasty. Outcomes included demographics, pre-post total joint arthroplasty Western Ontario McMaster score, and clinical outcomes (knee flexion and extension), as well as adverse events including infection rates. Infections were determined on clinical grounds and confirmed with laboratory investigations. In the injection group there were 18 cases of infected total joint arthroplasty versus 21 in the no-hyaluronic-acid-injection group. However, when the type of hyaluronic acid derivative was considered, more infections with higher versus lower molecular weight (15 vs. 3) were observed. Hence, prior intra-articular hyaluronic acid does not increase the risk of subsequent infection post total joint arthroplasty.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".