Validation of a Modified Version of the National Nosocomial Infections Surveillance System Risk Index for Health Services Research
Bibliographic record
Abstract
OBJECTIVE: To validate the National Nosocomial Infections Surveillance system risk index through administrative data to predict surgical site infections. DESIGN: Retrospective cohort study. SETTING: Population-based analysis in Ontario, Canada. PATIENTS: All elderly patients who underwent elective surgery from April 1, 1992, through March 31, 2006 (n = 469,349). METHODS: Data on procedural and patient outcomes were gathered from linked population-wide hospital discharge records and physician claims. The 75th percentile of surgical duration was estimated through anesthesiologist billing fees recorded in 15-minute increments; the American Society of Anesthesiology score of at least 3 out of 5 was estimated by diagnostic codes for severe systemic illness; and all surgeries were classified as clean or clean-contaminated because of their elective nature (thus, the maximum score on the modified index was 2). RESULTS: A total of 147,216 surgeries (31%) had a score of 0; 246,592 (53%) had a score of 1; and 75,541 (16%) had a score of 2 on the modified index. The 30-day risk of surgical site infection increased with each increment in the modified index (score of 0, 5.4%; score of 1, 8.0%; score of 2, 14.3%; P < .001). The association was evident for surgical site infection diagnosed during the index admission (score of 0, 2.0%; score of 1, 3.7%; score of 2, 8.9%; P < .001), as well as that associated with reoperation or death (score of 0, 0.04%; score of 1, 0.23%; score of 2, 0.73%; P < .001). The modified index predicted increases in surgical site infection risk within each of 11 surgical subgroups. In accord with past research, the modified index had modest discrimination (C statistic, 0.59), and the majority of surgical site infections (72%) occurred within lower risk strata. CONCLUSIONS: The modified index predicts surgical site infection in population-based analyses and is associated with incremental increases in risk.
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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 | 0.022 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".