Sex differences in crude mortality rates and predictive value of intensive care unit-based scores when applied to the cardiac intensive care unit
Bibliographic record
Abstract
Background: Limited data exists regarding sex differences in outcome and predictive accuracy of intensive care unit-based scoring systems when applied to cardiac intensive care unit patients. Methods: We reviewed medical records of patients admitted to cardiac intensive care unit from 1 January 2011–31 December 2016. Sex differences in mortality rates and the performance of intensive care unit-based scoring systems in predicting in-hospital mortality were analyzed. Calibration was assessed by the Hosmer-Lemeshow test and locally weighted scatterplot smoothing curves. Discrimination was assessed using the c statistic and receiver-operating characteristic curve. Results: Among 6963 patients, 2713 (39%) were women. Overall in-hospital and cardiac intensive care unit mortality rates were similar in women and men (9.1% vs 9.4%, p=0.67 and 5.9% vs 6%, p=0.88, respectively) and in age and major diagnosis subgroups. Of the scoring systems, Acute Physiology and Chronic Health Evaluation III and Sequential Organ Failure Assessment had poor calibration (Hosmer-Lemeshow p value <0.001), while Simplified Acute Physiology Score II performed better (Hosmer-Lemeshow p value 0.09), in both women and men. All scores had good discrimination (C statistics >0.8). In the subgroups of acute myocardial infarction and heart failure patients, all scores had good calibration (Hosmer-Lemeshow p>0.001) and discrimination (C statistic >0.8) while in diagnosis subgroups with highest mortality, the calibration varied among scores and by sex, and discrimination was poor. Conclusions: No sex differences in mortality were seen in cardiac intensive care unit patients. The mortality predictive value of intensive care unit-based scores is limited in both sexes and variable among different subgroups of diagnoses.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".