Hospital-Related Determinants For Surgical-Site Infection Following Hip Arthroplasty
Bibliographic record
Abstract
OBJECTIVE: To determine hospital-related risk factors for surgical-site infection (SSI) following hip arthroplasty. DESIGN: Prospective, multicenter cohort study based on surveillance data and data collected through a structured telephone interview. With the use of multilevel logistic regression, the independent effect of hospital-related characteristics on SSI was assessed. SETTING: Thirty-six acute care hospitals in the Dutch surveillance network for nosocomial infections (PREZIES), from 1996 to 2000. PATIENTS: Thirteen thousand six hundred eighty patients who underwent total or partial hip arthroplasty. RESULTS: A high annual volume of operations was associated with a reduced risk of SSI (risk-adjusted risk ratio [RR] per 50 extra operations, 0.85; 95% confidence interval [CI95], 0.74-0.97). With each extra full-time-equivalent infection control staff member per 250 beds available for prevention of SSI, the risk for SSI was decreased (RR, 0.48; CI95, 0.16-1.44), although the decrease was not statistically significant. Hospital size, teaching status, university affiliation, and number of surgeons and their years of experience showed no important association with the risk of SSI. CONCLUSION: Undergoing surgery in a hospital with a low volume of operations increases a patient's risk of SSI.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".