COVID-19 hospital designation: Effect on emergency department patient self-selection and volume
Bibliographic record
Abstract
Objective: Combating Coronavirus 2019 has stretched hospital resources to the extreme. In an effort to cohort personnel and equipment, NorthShore University HealthSystem (NSUHS) designated Glenbrook Hospital (GBH) as our “COVID hospital”, which became public knowledge on April 6, 2020. We hypothesize that with this public declaration our emergency department (ED) total volumes and COVID-19 related visits would be affected.Methods: We performed a retrospective analysis of our total ED volumes and COVID-19 related ED visits from March 12, 2020 until April 30, 2020. The pre public declaration timeframe of March 12-April 5, 2020 acted as our control whereas the post-public declaration acted as the testing group (April 6-April 30, 2020). NSUHS four primary hospitals were included in the analysis. We ran a chi-squared analysis on both groups to determine if there was statistical significance.Results: Both total ED volumes and COVID-19 related ED visits, when comparing pre VS post-public declaration of GBH as the “COVID hospital”, showed statistical significance (p < .001). Three of the four hospitals had a decrease in total ED volumes, whereas the COVID-19 related ED visits increased at two hospitals and decreased at the others.Conclusions: Our results support our hypothesis that after the public declaration of Glenbrook Hospital as the “COVID hospital”, patients’ decision making regarding which ED to visit was significantly affected. Certain limitations, including socioeconomic status and a small geographical footprint of NSUHS, may have affected our results. Further work should be done to reproduce these results to ensure replication.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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, 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".