A Risk Score Proposal for Covid-19 in Nursing Homes
Bibliographic record
Abstract
Background/Objectives: During the SARS-CoV-2 pandemic currently ongoing worldwide, several challenges were encountered in managing patients in out-of-hospital residences. The aim of this study is to establish a risk score indicating the probability of SARS-CoV-2 infection and estimate the seroprevalence in nursing homes. Design: This is a retrospective cross-sectional observational study. Participants and Setting: 231 patients (median age 86 years, min 53 max 100 years) were enrolled from three nursing homes of Pavia and its surroundings. Measurements: Medical history, clinical and instrumental data were correlated to the results of nasopharyngeal swab and serology. Results: Patients with positive nasopharyngeal swab and/or serology were 170 (74%, 95%CI: 67%-79%) and seroprevalence was 64%. Variables associated with COVID-19 infection used to build the clinical score were: anosmia and ageusia, pulse oximetry <90%, conjunctivitis, rhinorrhea, myalgia. The probability of COVID-19 positivity increased linearly over the clinical score values (score 0: 55%; score 1: 83%: score 2+: 95%). By adding lung ultrasound to the score, a Clinical & LUS Risk Score was created, which allowed further stratification. The area under the ROC Curve for the two models was 0∙73 and 0∙77, respectively. Of note, fever, one of the pivotal signs in COVID-19 patients, was not a common manifestation among nursing home residents with SARS-CoV-2 infection, therefore it was not included in the scores. Conclusions: Given the high SARS-CoV-2 seroprevalence in nursing homes, the Clinical San Matteo Risk Score, implemented by lung ultrasound when available, can help for an early identification, isolation and treatment of possible positive cases.
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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 | 0.009 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".