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 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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".