Are the Dysnatremias a Permanent Threat to the Critically Ill Patients?
Bibliographic record
Abstract
BACKGROUD: The dysnatremias (hyponatremia and hypernatremia) are relatively common findings on admission of intensive care unit (ICU) patients and may represent a major risk. The aim of the study was to assess the ability of serum sodium levels and the Acute Physiology and Chronic Health Evaluation II (APACHE II) to predict mortality of surgical critically ill patients. METHODS: One hundred and ninety-five surgical patients (62% males and 38% females; mean age of 51.8 ± 17.3 years) admitted to the ICU in the postoperative phase were retrospectively studied. The patients were divided into survivors (n = 152) and non-survivors (n = 43). APACHE II, and serum sodium levels at admission, 48 h and discharge were analyzed by generation of receiver operating characteristic (ROC) curves. RESULTS: The mean APACHE II was 16.3 ± 8.3 (13.6 ± 6.1 for survivors and 25.5 ± 8.5 for non-survivors). The area under the ROC curve for APACHE II was 0.841 (0.782 - 0.889) and 0.721 (0.653 - 0.783), 0.754 (0.653 - 0.783) and 0.720 (0.687 - 0.812) for serum sodium level at admission, 48 h and discharge, respectively. CONCLUSION: Even though APACHE II scoring system was the most effective index to predict mortality in the surgical critically ill patients, the serum sodium levels on admission may also be used as an independent predictor of outcome.
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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 | 0.001 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| 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".