Minimal Clinically Important Improvement of Routine Assessment of Patient Index Data 3 in Rheumatoid Arthritis
Bibliographic record
Abstract
OBJECTIVE: To compare the severity of illness of patients with systemic lupus erythematosus (SLE) between those hospitalized at academic medical centers and those hospitalized at community hospitals. METHODS: In this population based cross-sectional survey, data on all hospitalizations of patients with SLE in California, New York, and Pennsylvania in 2000 were obtained from discharge abstracts submitted by acute care hospitals to state health planning agencies. Patients hospitalized at one of 36 academic medical centers in these states (N = 2072) were compared to patients hospitalized at community hospitals (N = 9373). The primary measures of severity of illness were the SLE Comorbidity Index, a weighted index of SLE manifestations and comorbid medical conditions based on discharge diagnoses, and long lengths of stay, defined as stays that exceeded the 90th percentile of hospital stays in the same diagnosis-related group in the United States. RESULTS: Compared to patients at community hospitals, patients at academic medical centers had substantially higher scores on the SLE Comorbidity Index (odds ratio for each 1-point increase 1.27, 95% confidence interval 1.15-1.40, p < 0.0001) and were more likely to have long lengths of stay (OR 1.65, 95% CI 1.42-1.91, p < 0.0001). Patients at academic medical centers also had higher scores on the SLE Comorbidity Index (OR for each 1-point increase 1.16, 95% CI 1.07-1.27, p = 0.0002) and were more likely to have long lengths of stay (OR 1.27, 95% CI 1.08-1.49, p = 0.004) compared to patients at large (> or = 300 beds) community hospitals in the same metropolitan areas. Results for the SLE Comorbidity Index were similar in the subset of patients with SLE as the primary discharge diagnosis. CONCLUSION: Patients with SLE hospitalized at academic medical centers are generally more severely ill than those hospitalized at community hospitals, including large community hospitals in the same area.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".