Using the Clinical Frailty Scale in Allocating Scarce Health Care Resources
Bibliographic record
Abstract
The key idea behind the Clinical Frailty Scale (CFS) is that, as people age, they are more likely to have things wrong with them. Those things they have wrong (health deficits) can, as they accumulate, erode their ability to do the high order functions which define their overall health. These high order functions include being able to: think and do as they please; look after themselves; interact with other people; and move about without falling. The Clinical Frailty Scale brings that information together in one place. This paper is a guide for people new to the Clinical Frailty Scale. It also introduces an updated version (CFS version 2.0), with revised level names (e.g., "vulnerable" becomes "living with very mild frailty") and minor edits to level descriptions. The key points discussed are that the Clinical Frailty Scale assays the baseline state, it is not widely validated in younger people or those with stable single-system disabilities, and it requires clinical judgement. The Clinical Frailty Scale is now commonly used as a triage tool to make important clinical decisions such as allocating scarce health care resources for COVID-19 management; therefore, it is important that the scale is used appropriately.
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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 | 0.010 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".