Avaliação do índice de comorbidade de Charlson em internações da região de Ribeirão Preto, São Paulo, Brasil
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The objective of this article was to evaluate the use of the Charlson comorbidity index (CCI) to predict inpatient death in Ribeirão Preto, São Paulo State, Brazil. 54,680 hospitalizations from January 1996 to December of 1997 were analyzed. Two International Classification of Diseases adaptations of CCI were compared, and the 30 clinical conditions assessed by the Charlson index were reviewed. Logistic regression was used to evaluate the models' capacity to predict inpatient death. The baseline model included: age, sex, and principal diagnosis. Differences in ICD adaptations showed little effect on the models' discriminatory capacity. Revision of the 30 clinical conditions increased the model's discriminatory capacity to predict death (C statistic = 0.73), as compared to the model with the original CCI (C statistic = 0.72). All tested models had a reduced effect on the baseline model's discriminatory capacity (C statistic = 0.70). The results show the importance of Brazil having an information system that allows a complete description of hospital morbidity in order to monitor health service performance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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 it