Ottawa Risk Scale in Predicting the Outcome of Chorionic Obstructive Pulmonary Disease Exacerbation in Emergency Department; a Diagnostic Accuracy Study.
Bibliographic record
Abstract
Introduction: The disposition decision is a great challenge for clinicians in managing patients with chronic obstructive pulmonary disease (COPD) exacerbation. This study aimed to evaluate the accuracy of Ottawa COPD Risk Scale (OCRS) in predicting the short-term adverse events in the mentioned patients. Methods: This prospective diagnostic accuracy study was conducted on COPD exacerbation cases who were referred to the emergency department (ED). Patients were followed up for 30 consecutive days for adverse events including the need for intubation, non-invasive ventilation, myocardial infarction, readmission, and death from any cause, and finally the accuracy of OCRS in predicting the outcome was evaluated. Results: 362 patients with the mean age of 65.55 ± 10.65 (6- 95) years were evaluated (58.0% male). Among the patients, 164 (45.3%) cases were discharged from ED, and 198 (54.7%) were admitted to the hospital. 136 (37.6%) cases experienced at least one of the studied short-term adverse events. The mean OCSD score of this series was 1.96 ± 2.39 (0 - 10). The area under the curve of OCRS in predicting the outcome of COPD patients was 0.814 (95%CI: 0.766 - 0.862). The best cut-off point of the scale in predicting the outcome was 1.5. The sensitivity and specificity of the scale were 75.75% (95%CI: 69.6% - 81.42%) and 89.63% (95%CI: 83.67% - 93.66%), respectively. By employing this threshold, 48 (13.25%) cases would have unnecessary hospitalization, and 17 (0.04%) would be discharged incorrectly. Conclusion: The OCRS has acceptable level of prediction accuracy in predicting the short-term adverse event of COPD patients. The use of this scoring in the routine practice of ED clinicians can lead to a reduction in unnecessary admissions and unsafe discharge for these patients.
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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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".