Evaluation of visual triage for screening of Middle East respiratory syndrome coronavirus patients
Bibliographic record
Abstract
The emergence of Middle East respiratory syndrome coronavirus (MERS-CoV) in September 2012 in Saudi Arabia had attracted the attention of the global health community. In 2017 the Saudi Ministry of Health released a visual triage system with scoring to alert healthcare workers in emergency departments (EDs) and haemodialysis units for the possibility of occurrence of MERS-CoV infection. We performed a retrospective analysis of this visual score to determine its sensitivity and specificity. The study included all cases from 2014 to 2017 in a MERS-CoV referral centre in Riyadh, Saudi Arabia. During the study period there were a total of 2435 suspected MERS cases. Of these, 1823 (75%) tested negative and the remaining 25% tested positive for MERS-CoV by PCR assay. The application of the visual triage score found a similar percentage of MERS-CoV and non-MERS-CoV patients, with each score from 0 to 11. The percentage of patients with a cutoff score of ≥4 was 75% in patients with MERS-CoV infection and 85% in patients without MERS-CoV infection (p 0.0001). The sensitivity and specificity of this cutoff score for MERS-CoV infection were 74.1% and 18.6%, respectively. The sensitivity and specificity of the scoring system were low, and further refinement of the score is needed for better prediction of MERS-CoV infection.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".