Bibliographic record
Abstract
BACKGROUND: Adjustment for comorbidity is an important component of any clinical outcome study using administrative data. The Elixhauser Index is a relatively newer comorbidity index for use with administrative data and has not been used to assess prognosis in patients with stroke. Similarly, an International Classification of Diseases (ICD)-10 coding algorithm has been rarely reported for Elixhauser Index. OBJECTIVE: To evaluate whether the Elixhauser Index provides a useful comorbidity adjustment for predicting in-hospital case-fatality in stroke outcome studies and to compare the degree of consistency using ICD-9-CM and ICD-10 coding algorithms. METHODS: Patients who had stroke from 1998 to 2000 (cohort A in the ICD-9-CM data) and 2003 to 2005 (cohort B in the ICD-10 data) in a large Canadian city were identified from the Hospital Discharge database. The performance of two coding algorithms for predicting the in-hospital case-fatality was assessed using multivariable logistic regression models. The C-statistic was used to compare the performance of each coding algorithm in predicting in-hospital case-fatality. RESULTS: Among 2,465 patients with stroke in the ICD-9-CM data (cohort A) and 2,987 patients with stroke in the ICD-10 data (cohort B), there was no difference in model performance using ICD-9-CM (C-statistic was 0.717) as compared with ICD-10 coding algorithms (C-statistic was 0.721; p = 0.83). Elixhauser comorbidity adjustment provided a better prediction of in-hospital case-fatality compared to reduced models including only age and gender (p < 0.0001) for both coding models. CONCLUSION: The Elixhauser Index provides similar comorbidity adjusted risk estimates using both ICD-9-CM and ICD-10, and may be useful for predicting risk-adjusted in-hospital case-fatality in stroke outcome studies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".