The correlation of the Sequential Organ Failure Assessment score with intensive care unit outcome
Bibliographic record
Abstract
We conducted a prospective observational review of 100 consecutive patients admitted to our ICU. We collected data relating to daily maximum organ dysfunction scores. Outcome was defined in terms of length of ICU stay and ICU mortality. We included 100 patients (62 males), mean age 60.9 years. Of these admissions, 45 were elective surgical, 22 emergency surgical, 33 medical. The median Sequential Organ Failure Assessment (SOFA) score on admission was 4.50 (IQR 4). The median maximum SOFA score was 5.00 (IQR 5). The median length of ICU stay was 3.0 days (IQR 3). The overall ICU mortality rate was 14.0%. For patients with a maximum SOFA score ≤8, mortality was 5.1% – vs 45.5% for those whose maximum SOFA score was >8 ( P < 0.001). Sixty-four per cent of patients scored their maximum SOFA score on admission. In patients whose SOFA score increased after admission, the mortality was 24.3%. Logistic regression analysis showed the maximum SOFA score bore a stronger correlation with mortality than admission SOFA score. See Figure 1 . Median length of ICU stay in patients with an admission SOFA score >8 vs those whose admission SOFA score was ≤8 ( P = 0.001). Maximum and admission SOFA scores are of prognostic value in the intensive care setting; allowing patients with increased risk of mortality and prolonged stay to be identified.
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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".