Retrospective frailty determination in critical illness from a review of the intensive care unit clinical record
Bibliographic record
Abstract
Frailty is one of the major challenges for intensive care, affecting one-third of intensive care unit patients and being associated with a range of poor health outcomes. Determination of frailty in critical illness using the Clinical Frailty Scale has recently been adopted by the Australian and New Zealand Intensive Care Society, but it is not known whether this is able to be measured from the clinical record without interviewing patients or their relatives. The aims of this retrospective cohort study were to test whether a Clinical Frailty Scale score could be assigned in an intensive care unit population from the clinical record, and to assess the inter-rater reliability of frailty measured in this manner. A total of 144 patients were enrolled. Of these, 137 (95%) were able to have a Clinical Frailty Scale score assigned, and 22 (15%) were scored as frail (Clinical Frailty Scale ≥5). Cohen’s kappa coefficient for inter-rater reliability between assessors was 0.67, confirming substantial agreement. Consistent with other critically ill cohorts, frailty was associated on multivariate analysis with age, Charlson comorbidity score, dependence with activities of daily living, and limitation of medical treatment, indicating validity of this approach to frailty measurement. Our results imply that frailty measurement is possible and feasible from the intensive care unit clinical record, which is of importance as routine measurement and reporting of frailty in intensive care units in our region increases. Future work should seek to validate an assigned Clinical Frailty Scale score with that obtained directly from patients or their next of kin.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.016 | 0.064 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".