The Extra length of stay, costs, and mortality associated with healthcare-associated infections (HCAI) at a referral hospital: a prospective nested case control study
Bibliographic record
Abstract
Abstract Background:healthcare-associated infections (HCAIs) are one of the real risks in any health system and have a considerable effect on increased morbidity, mortality, and financial burden. The present study aims to investigate the mortality rate, length of stay, and hospitalization cost in patients with and without HCAI.Methods:A prospective cohort study was conducted on 396 Patients with and without HCAI. They were matched with the patients in the same ward and at the same time. This study was done in an educational hospital in Ahvaz with 800 beds for five months. Descriptive analyses were done based on total direct costs, LOS, and mortality rates in patients with and without HCAI. The magnitude of the relationship between risk factors and HCAI was quantified using the odds ratio (OR). Logistic regression was used to calculate the OR.Results:The most common HCAI and microorganisms were UTIs and E. coli. Infected patients had longer hospitalization times (mean 19.58 vs. 7.62, P < 0.05). The mortality rate in cases increased significantly compared with the uninfected group (22% vs. 4.7% P < 0.05). When compared with those who did not develop an infection (control group), infection was associated with significantly higher treatment cost (7399.13±9631.98) (2765.19±2999.33), (P< 0.001).Conclusions:An infection acquired during a hospital stay was associated with higher hospitalization costs, prolonged hospitalization, and a significant increase in the rate of mortality.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".