Critically ill healthcare workers with the middle east respiratory syndrome (MERS): A multicenter study
Bibliographic record
Abstract
BACKGROUND: Middle East Respiratory Syndrome Coronavirus (MERS-CoV) leads to healthcare-associated transmission to patients and healthcare workers with potentially fatal outcomes. AIM: We aimed to describe the clinical course and functional outcomes of critically ill healthcare workers (HCWs) with MERS. METHODS: Data on HCWs was extracted from a multi-center retrospective cohort study on 330 critically ill patients with MERS admitted between (9/2012-9/2015). Baseline demographics, interventions and outcomes were recorded and compared between survivors and non-survivors. Survivors were approached with questionnaires to elucidate their functional outcomes using Karnofsky Performance Status Scale. FINDINGS: Thirty-Two HCWs met the inclusion criteria. Comorbidities were recorded in 34% (11/32) HCW. Death resulted in 8/32 (25%) HCWs including all 5 HCWs with chronic renal impairment at baseline. Non-surviving HCW had lower PaO2/FiO2 ratios 63.5 (57, 116.2) vs 148 (84, 194.3), p = 0.043, and received more ECMO therapy compared to survivors, 9/32 (28%) vs 4/24 (16.7%) respectively (p = 0.02).Thirteen of the surviving (13/24) HCWs responded to the questionnaire. Two HCWs confirmed functional limitations. Median number of days from hospital discharge until the questionnaires were filled was 580 (95% CI 568, 723.5) days. CONCLUSION: Approximately 10% of critically ill patients with MERS were HCWs. Hospital mortality rate was substantial (25%). Patients with chronic renal impairment represented a particularly high-risk group that should receive extra caution during suspected or confirmed MERS cases clinical care assignment and during outbreaks. Long-term repercussions of critical illness due to MERS on HCWs in particular, and patients in general, remain unknown and should be investigated in larger studies.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".