Preoperative Antibiotics and Mortality in the Elderly
Bibliographic record
Abstract
OBJECTIVE AND BACKGROUND: It is generally thought that the use of preoperative antibiotics reduces the risk of postoperative infection, yet few studies have described the association between preoperative antibiotics and the risk of dying. The objective of this study was to determine whether preoperative antibiotics are associated with a reduced risk of death. METHODS: We performed a multivariate matched, population-based, case-control study of death following surgery on 1362 Pennsylvania Medicare patients between 65 and 85 years of age undergoing general and orthopedic surgery. Cases (681 deaths within 60 days from hospital admission) were randomly selected throughout Pennsylvania using claims from 1995 and 1996. Models were developed to scan Medicare claims, looking for controls who did not die and who were the closest matches to the previously selected cases based on preoperative characteristics. Cases and their controls were identified, and charts were abstracted to define antibiotic use and obtain baseline severity adjustment data. RESULTS: For general surgery, the odds of dying within 60 days were less than half in those treated with preoperative antibiotics within 2 hours of incision as compared with those without such treatment: (odds ratio = 0.44; 95% confidence interval, 0.32-0.60), P < 0.0001). For orthopedic surgery, no significant mortality reduction was observed (OR = 0.85; 95% confidence interval, 0.54-1.32; P < 0.464). INTERPRETATION: Preoperative antibiotics are associated with a substantially lower 60-day mortality rate in elderly patients undergoing general surgery. In patients who appear to be comparable, the risk of death was half as large among those who received preoperative antibiotics.
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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".