Age is just a number – and so is frailty: Strategies to inform resource allocation during the COVID-19 pandemic
Bibliographic record
Abstract
As cases of critical COVID-19 patients have taxed their resources, hospitals in China and Europe have faced the difficult task of establishing criteria for which patients receive which level of care. Hospitals in Italy during this pandemic seem overwhelmed, leaving physicians with little guidance on how to triage patients and allocate therapeutic resources. 1 Based on current rates of critical care admission in China and Italy, in a worstcase scenario, Canada would have a deficit of thousands of intensive care unit (ICU) beds in the peak of a national epidemic-a problem that will disproportionately affect older adults. This highlights the necessity of sound geriatric principles in the emergency department (ED) that incorporate the essential concept of frailty. We propose that a structured, evidence-based assessment of frailty, and not just noting the person's age, will help guide ED care during the COVID-19 pandemic.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".