The pictorial fit-frail scale: a novel tool for frailty assessment in critically ill older adults
Bibliographic record
Abstract
BACKGROUND: Frailty is a state of high vulnerability to adverse health outcomes. It is an important factor influencing the prognosis of older, critically ill patients. Several methods to assess frailty were evaluated in the critical care setting. The Pictorial Fit-Frail Scale (PFFS) is a validated quick and easy-to-use tool for frailty assessment. It takes < 5 min to fill by the patient or caregiver; it requires no clinical examination by medical staff. This study evaluated the use of the PFFS in an intensive care unit (ICU). METHODS: A single-center retrospective study, performed in an 18-bed mixed medical-surgical ICU in a university-affiliated tertiary hospital. As of 1/9/2022, all older patients are routinely asked to fill out the PFFS. Patients were grouped based on their PFFS score. Baseline characteristics and admission outcomes were compared. Correlation between the PFFS and prognostic scores was examined. Mortality was analyzed using logistic and Cox regressions. RESULTS: 168 patients were included. 56 (33.33%) patients were non-frail, 81 (48.21%) were mildly-moderately frail, and 31 (18.45%) were severely frail. There were no differences in baseline characteristics or prognostic scores between frailty groups. No correlation was found between PFFS, age, APACHE2, and SOFA24. Multivariate logistic regression demonstrated an association between frailty and 90d but not with ICU mortality. Cox regression demonstrated higher mortality in the mild-moderate frailty (HR 2.053, 95%CI 1.009, 4.179) and severe frailty (HR 4.353, (95% CI 1.934, 9.801)) groups compared to the non-frail group. CONCLUSION: Frailty assessment by the PFFS in the ICU is feasible. Frailty is a distinct characteristic of older, critically ill patients and is independently associated with 90d mortality.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".