Bibliographic record
Abstract
Introduction: The comorbidity measures are useful in comparing patient population in health service search, predicting inpatient mortality, health care cost etc. Amongst the two most widely used comorbidity measures, the Elixhauser's has been reported to perform better than the Charlson's in predicting short and long-term mortality. The Elixhauser comorbidity measure consists of 30 comorbidities from the international disease classification - 9 clinical modification (ICD-9CM). To improve ease in the use, a point system was derived for Elixhauser comorbidities using multiple logistic-regression to predict inpatient mortality in single center Canadian cohort. The derived index performed well in split sample validation. Methods: This retrospective observational study was performed at 300-bed community hospital catering a suburban population. All the adult patients admitted during October 2005 through September 2011 were included in the study. Our hospital uses ICD-9 CM and coding is performed by expert coders. Score allotment for the comorbidities was performed as described by Walraven et al (Med Care. 2009 Jun;47(6):626-33). Performance of the index in predicting inpatient mortality was measured using the area under the Receiver operating curve. Predicted and observed mortality were compared. Results: Total of 52,068 patients were admitted during this period. Mean age was 65.9 (SD 18.9) years, 41.4% were male and median duration of hospital stay was 3 days (IQR 2 to 5). 1037 (1.99%) patients expired in the hospital. Area under the Receiver operating curve in predicting all cause inpatient mortality was 0.80 (95% CI, 0.78-0.81). The predicted and observed inpatient mortality were similar, Fisher's exact test 2-tailed p = 0.1487 Conclusions: We report excellent performance of the Elixhauser comorbidity index in prediction of inpatient mortality in an external cohort from United States.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.572 | 0.493 |
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".