Risk of COVID-19 Outcomes among Healthcare Workers: Findings from the Philippine CORONA Retrospective Cohort Study
Bibliographic record
Abstract
Objectives: While many healthcare workers (HCWs) contracted COVID-19 during the pandemic, more information is needed to fully understand the potential for adverse health effects in this population segment. The aim of the present study is to examine the association between healthcare worker status and neurologic and clinical outcomes in COVID-19 infected inpatients. Methods: Using the nationwide database provided by the retrospective cohort Philippine CORONA study, we extracted relevant data and performed a secondary analysis primarily focusing on the presentation and outcomes of healthcare workers. Propensity score matching in a 3:1 ratio was performed to match HCWs and non-HCWs. We performed multiple logistic and Cox regression analyses to determine the relationship between HCWs and COVID-19 clinical outcomes. Results: We included 3,362 patients infected with COVID-19; of which, 854 were HCWs. Among the HCWs, a total of 31 (3.63%) and 45 (5.27%) had the primary outcomes of in-hospital mortality and respiratory failure, respectively. For both overall and 3:1 propensity-matched cohorts, being an HCW significantly decreased the odds of the following outcomes: severe/critical COVID-19 at nadir; in-hospital mortality; respiratory failure; intensive care unit admission; and hospital stay >14 days. Conclusion: We found that being an HCW is not associated with worse neurologic and clinical outcomes among patients hospitalized for COVID-19.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".