1366. Temporal changes in Demographics, Characteristics and Treatments for Patients enrolled in the Canadian Treatments for COVID-19 Trial
Bibliographic record
Abstract
Abstract Background Over 4 million Canadians have been infected with COVID-19. The risk to the Canadian population has been dynamic over time, with changing demographics of patients at risk, differential uptake of vaccination, and potentially differential presentation, access, or acceptance of evolving treatments. Our study aims to evaluate the temporal change in patient characteristics, process of care and outcomes over the pandemic for patients with COVID-19 admitted to hospitals and enrolled in the Canadian Treatments for COVID-19 trial (CATCO). Methods The study included all patients admitted to 52 participating Canadian hospitals with laboratory confirmed SARS-COV-2 infection and enrolled in the CATCO trial. Data was analyzed temporally over six periods of enrollment, corresponding to approximately every 242-294 patients (Figure 1). Patient characteristics and outcomes were summarized using descriptive statistics (i.e., median, proportions). Results Mean age (63 years) and sex (30% female) among enrolled patients were similar across six pandmiec periods. Patient admission to ICU was most common at the beginning of the pandemic (period 1 n=75, 30.4%; period 2 n=67, 27.2%; approximately 20% in subsequent periods [p< 0.001]). The proportion of patients who identified as Black decreased from 10.2% to 3.1% between the first and sixth period, while the proportion of other minority groups remained stable (Figure 2.). Treatment with corticosteroids increased substantially after the first period (41.2% to over 90% in each subsequent period). Unadjusted in-hospital and 60-day mortality was similar over periods (p=0.501). Conclusion There were changes in treatments and a decreasing proportion of enrolled patients admitted to ICU as enrollment in CATCO progressed. In contrast to epidemiological data that showed a change in demographics of hospitalized persons shifting from predominantly Caucasians to predominantly minority groups overtime, our study found that there was not increasing enrollment of minority groups over time. In a dynamic pandemic, it may be important to include the potential for temporal changes in patient characteristics, treatments, and support when investigating the effect of medications on clincial outcomes over time. Disclosures All Authors: No reported disclosures
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".