Assessment of a universal preprocedural screening program for coronavirus disease 2019 (COVID-19)
Bibliographic record
Abstract
To the Editor-The novel coronavirus 2019 (COVID-19) has caused a global pandemic, placing an unprecedented strain on the US healthcare system.On March 12, 2020, to preserve the safety of hospital staff and patients during the pandemic, the US Department of Health and Human Services and the American College of Surgeons issued a guidance for hospitals and healthcare systems to postpone elective procedures. 2 Similar guidance followed from the US Surgeon General and the US Centers for Medicare and Medicaid Services, operationalized by individual states. 3,4Decreased surgical capacity from COVID-19 has affected healthcare economic and patient outcomes.As a frame of reference, deferred elective surgical activity in 2003 during the severe acute respiratory syndrome (SARS) pandemic resulted in an estimated $32.1 million in direct cost to hospitals in the Toronto and greater Toronto area 5 and uninteded consequences, such as seriously ill patients not seeking care. 6s states have gradually allowed elective procedures to resume in the United States, healthcare organizations have been responsible for mitigating the spread of severe acute respiratory coronavirus virus 2 (SARS-CoV-2), the virus that causes COVID-19.In particular, although the importance of screening all patients with and without symptoms has been recognized, some still question the value of universal screening given economic and operational considerations.In this study, we aimed (1) to determine the value of universal preprocedural screening for a representative academic health center and (2) to determine the safety of resuming elective procedures using the volume of asymptomatic positive screens.
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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 | 0.009 | 0.065 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".