Prediction of Personal Protective Equipment Use in Hospitals During\n COVID-19
Bibliographic record
Abstract
Demand for Personal Protective Equipment (PPE) such as surgical masks,\ngloves, and gowns has increased significantly since the onset of the COVID-19\npandemic. In hospital settings, both medical staff and patients are required to\nwear PPE. As these facilities resume regular operations, staff will be required\nto wear PPE at all times while additional PPE will be mandated during medical\nprocedures. This will put increased pressure on hospitals which have had\nproblems predicting PPE usage and sourcing its supply. To meet this challenge,\nwe propose an approach to predict demand for PPE. Specifically, we model the\nadmission of patients to a medical department using multiple independent\nqueues. Each queue represents a class of patients with similar treatment plans\nand hospital length-of-stay. By estimating the total workload of each class, we\nderive closed-form estimates for the expected amount of PPE required over a\nspecified time horizon using current PPE guidelines. We apply our approach to a\ndata set of 22,039 patients admitted to the general internal medicine\ndepartment at St. Michael's hospital in Toronto, Canada from April 2010 to\nNovember 2019. We find that gloves and surgical masks represent approximately\n90% of predicted PPE usage. We also find that while demand for gloves is driven\nentirely by patient-practitioner interactions, 86% of the predicted demand for\nsurgical masks can be attributed to the requirement that medical practitioners\nwill need to wear them when not interacting with patients.\n
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".