Age Alone is not Adequate to Determine Healthcare Resource Allocation during the COVID-19 Pandemic
Bibliographic record
Abstract
BACKGROUND: The Canadian Geriatrics Society (CGS) fosters the health and well-being of older Canadians and older adults worldwide. Although severe COVID-19 illness and significant mortality occur across the lifespan, the fatality rate increases with age, especially for people over 65 years of age. The dichotomization of COVID-19 patients by age has been proposed as a way to decide who will receive intensive care admission when critical care unit beds or ventilators are limited. We provide perspectives and evidence why alternative approaches should be used. METHODS: Practitioners and researchers in geriatric medicine and gerontology have led in the development of alternative approaches to using chronological age as the sole criterion for allocating medical resources. Evidence and ethical based recommendations are provided. RESULTS: Age alone should not drive decisions for health-care resource allocation during the COVID-19 pandemic. Decisions on health-care resource allocation should take into consideration the preferences of the patient and their goals of care, as well as patient factors like the Clinical Frailty Scale score based on their status two weeks before the onset of symptoms. CONCLUSIONS: Age alone does not accurately capture the variability of functional capacities and physiological reserve seen in older adults. A threshold of 5 or greater on the Clinical Frailty Scale is recommended if this scale is utilized in helping to decide on access to limited health-care resources such as admission to a critical care unit and/or intubation during the COVID-19 pandemic.
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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 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| 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".