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 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 | 0.017 | 0.082 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".