Comparing the APACHE II, SOFA, LOD, and SAPS II scores in patients who have developed a nosocomial infection
Bibliographic record
Abstract
Background: There have been numerous scores intended to evaluate the severity of patients condition upon admission and during their intensive care unit (ICU) stay. However, to our knowledge, no study has ever evaluated the predictive abilities of these scores among nosocomial patients during their ICU stay. The aim of our study is to compare the predictive performances of the Acute Physiology, and, Chronic Health Evaluation (APACHE II) score, Simplified Acute Physiologic Score (SAPS II), Logistic Organ Dysfunction (LOD), and Sequential Organ Failure Assessment (SOFA) scores among intensive care patients who have developed a nosocomial infection. Methods: The study is monocentric and retrospective. The APACHE II, SAPS II, LOD, and SOFA scores were reported from the third day of the patients hospital stay, preceding the diagnosis of the first nosocomial event up to the third post diagnosis day. Results: Out of 46 patients contracting at least one ICU-acquired infection, the multiple analyses indicated that on the day of diagnosis, the SOFA score is the most predictive (odds ratio [OR]: 12.3; 95% confidence interval [CI]: 2.3364.91). The second most predictive was the APACHE II score (OR: 8.29; 95% CI: 1.4348.14). The third and fourth most predictive were the LOD score (OR: 4.06; 95% CI: 0.8120.26) and the SAPS II score (OR: 2.26; 95% CI: 0.559.24), respectively. Conclusion: The analysis of the receiver operating characteristic areas under the curve of the reported scores in the present study showed that the best predictive performance is in favor of the SOFA score. DOI: http://dx.doi.org/10.3329/bccj.v2i1.19949 Bangladesh Crit Care J March 2014; 2 (1): 4-9
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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 | 0.007 |
| 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 | 0.001 |
| Research integrity | 0.001 | 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".