From pandemic to endemic: A comparison of first, second, and third waves of COVID-19 for applicability in communicable disease management
Bibliographic record
Abstract
Background: The COVID-19 pandemic created pressure on healthcare systems worldwide. Hospitals have developed strategies to efficiently address the demand for inpatient beds.Objective: This paper examines changes in length of stay at a southern academic medical center and documents the intervention efforts aimed at providing high quality care and reduced lengths of stay.Methods: Data include 3,279 patients receiving inpatient treatment for COVID-19 between March 29, 2020, and October 31, 2021. The study data mirrors the three major waves of COVID-19 pandemic in Alabama as reported in Johns Hopkins’ coronavirus resource center. To account for the chronological change in care processes, we interviewed Hospitalists and categorized the interventions by month, June 2020-February 2021. We examined changes in average length of stay and differences in sociodemographic characteristics among the three waves using ANOVA and chi-square tests. Socio demographic factors analyzed include age, gender, race/ethnicity, marital status, and insurance.Results: The average length of stay, ICU admissions, and 30-day readmissions each decreased in the second and third waves compared to the first wave. Statistically significant differences were found for ICU admission, age, and insurance for hospitalized patients among waves.Conclusions: This study contributes to the COVID-19 literature by providing the chronological evolution of ALOS and interventions during the pandemic by highlighting the case of a southern academic medical center.
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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".