Administrative data outperformed single-day chart review for comorbidity measure
Bibliographic record
Abstract
OBJECTIVE: The purpose of this article is to compare the Charlson comorbidity index derived from a rapid single-day chart review with the same index derived from administrative data to determine how well each predicted inpatient mortality and nosocomial infection. DESIGN: Cross-sectional study. SETTING: The study was conducted in the context of the Swiss Nosocomial Infection Prevalence (SNIP) study in six hospitals, canton of Valais, Switzerland, in 2002 and 2003. PARTICIPANTS: We included 890 adult patients hospitalized from acute care wards. MAIN OUTCOME MEASURES: The Charlson comorbidity index was recorded during one single-day for the SNIP study, and from administrative data (International Classification of Disease, 10th revision codes). Outcomes of interest were hospital mortality and nosocomial infection. RESULTS: Out of 17 comorbidities from the Charlson index, 11 had higher prevalence in administrative data, 4 a lower and two a similar compared with the single-day chart review. Kappa values between both databases ranged from - 0.001 to 0.56. Using logistic regression to predict hospital outcomes, Charlson index derived from administrative data provided a higher C statistic compared with single-day chart review for hospital mortality (C = 0.863 and C = 0.795, respectively) and for nosocomial infection (C = 0.645 and C = 0.614, respectively). CONCLUSIONS: The Charlson index derived from administrative data was superior to the index derived from rapid single-day chart review. We suggest therefore using administrative data, instead of single-day chart review, when assessing comorbidities in the context of the evaluation of nosocomial infections.
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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 | 0.009 | 0.006 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".