RAPID score in Covid-19 patients: a clinical-radiological index for the safe discharge from the Emergency Department. A preliminary report
Bibliographic record
Abstract
To evaluate the performance of a clinical-radiological index (RAPID-Covid score) in achieving Safe Discharge (SD) of patients accessing the Emergency Department (ED) with symptoms suggesting Covid-19. Clinical and radiological data were retrospectively collected from 853 consecutive patients admitted to the ED during the pandemics with symptoms suggesting Covid-19. Illness severity was graded with RAPID-Covid score, composed of chest X-ray findings, clinical symptoms and PaO2/FiO2. Patients with RAPIDCovid score ≥5 were admitted. Primary outcome was SD of patients to home care. SD was defined as survival of the patient, without evidence of second access to ED requiring hospitalization. 212/853 patients were discharged. 27/212 had a score ≥5 but refused admission. 185/212 were discharged with score <5: 147/185 (79,5%) survived and did not re-access ED; 1/185 (0,5%) died at home after first ED-dismissal; 37/185 (20,0%) had a second access. Of these 15/37 (8,1%) were newly dismissed and one of them (1/15) died at home; 22/37 (11,9%) were hospitalized, 1/22 died during hospitalization. SD was obtained in 161/185 patients (87%). Readmissions occurred 5,1±2,6days from first discharge. Follow-up was 16,7±6,0days. RAPID-Covid score proves useful for SD of Covid-19 to home care. 6-10days may further increase confidence.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".