Cross‐Cultural Adaptation of the Clinical Frailty Scale for Critically Ill Patients in Spain and Concurrent Validity With <scp>FRAIL</scp>‐Es
Bibliographic record
Abstract
AIMS: To adapt the Clinical Frailty Scale (CFS) into Spanish and assess its concordance with the Spanish version of the FRAIL scale (FRAIL-Es) in the context of intensive care. DESIGN: Validation study of frailty assessment scales in critically ill patients. METHODS: The study was conducted in two phases. The first phase consisted of translating, culturally adapting, and validating the CFS into Spanish. The second phase consisted of a metric descriptive study to assess the concurrent criterion validity of the adapted CFS with FRAIL-Es in a cohort of intensive care patients. Both scales were assessed upon admission to intensive care and at 3, 6, 9, and 12 months post-hospital discharge. Analysis was performed using T-Student/Mann-Whitney, chi-squared and Cohen's Kappa tests. RESULTS: Successful adaptation of the CFS with minimal changes was achieved, demonstrating its applicability in the evaluated context. The pilot study indicated that CFS-Es is easy to assess, but some subjectivity in interpretation was noted. CFS-Es and FRAIL-Es were applied to 212 patients, revealing variations in frailty prevalence. The concordance and correlation between the CFS and FRAIL scales are robust. These differences suggest that the choice of scale may impact the identification of frail patients. These results emphasise the importance of considering specific characteristics of each scale when assessing frailty in critically ill patients, providing valuable information for clinical implementation and research in this field. PATIENT OR PUBLIC CONTRIBUTION: Assessing frailty upon admission can be helpful in the care of frail patients, allowing the development of specific care plans based on pre-existing frailty.
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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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".