Bibliographic record
Abstract
Abstract Since the beginning of the SARS-CoV 2 pandemic, healthcare authorities have made clear that it is crucial to track and identify COVID-19 symptoms and seek medical attention in the presence of the first warning signs, as immediate medical attention can improve the patient’s prognosis. Therefore the present work aims to analyze the risks associated with the time between the patient’s first symptoms and hospitalization followed by death. A cross-sectional study was performed among Mexican population diagnosed with COVID-19 and hospitalized from March to January 2021. Four different Bayesian models were developed to asses the risk associated with different patient trajectories: symptoms-hospitalization and hospitalization-death. Comorbidities that could worsen the patient outcome were included as linear predictions; these analyses were further broken down to the different states of the Mexican Republic and the healthcare providers within. Model III was chosen as the best performance through a validation of leaving one out (LOO). Increased risk for hospitalization was observed at the global population level for chronic renal disease, whereas for death such was the case for COPD and the interaction of diabetes:hypertension:obesity. Our results show that there are differences in mortality between the states without accounting for institution and it is related to the prompt time of death or viceversa. Regarding the 6 healthcare providers included in the analysis differences were also found. While state-managed hospitals and private sector showed lower risks, in contrast the IMSS seems to be the one with the highest risk. The proposed modelling can be helpful to improve healthcare assistance at a regional level, additionally it could inform statistical parameter inference in epidemiological models.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.018 | 0.005 |
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".