Predicting Intensive Care and Hospital Outcome with the Dalhousie Clinical Frailty Scale: A Pilot Assessment
Bibliographic record
Abstract
Frailty may help to predict intensive care unit (ICU) patient outcome. The Dalhousie Clinical Frailty Scale (DCFS) is validated to assess frailty in ambulatory settings but has not been investigated in Australian ICUs. We conducted a prospective three-month study of patients admitted to a tertiary level ICU. Within 24 hours of ICU admission, the next of kin or nurse in charge assigned a DCFS score to the patient. Data were obtained to assess the association between frailty and patient outcome. The DCFS score was completed in 205 of 348 (59%) of eligible patient admissions. The mean DCFS score was 3.2 (±1.6). Overall frailty (DCFS>4) occurred in 28 of 205 patients (13%, confidence interval 9% to 17%), 13 of 93 (15%, confidence interval 10% to 25%) in patients aged >65 years and 5 of 11 (45%, confidence interval 21% to 71%) in those>85 years. Patients with chronic liver disease (P<0.001) and end-stage renal failure (P=0.009) were more likely to be frail. The DCFS score was not significantly associated with ICU or hospital mortality: odds ratio 0.98 (95% confidence interval 0.6 to 1.6) and odds ratio 1.07 (95% confidence interval 0.8 to 1.4), respectively. However, after adjustment for illness severity and requirement for palliative care, the DCFS score was significantly associated with increased (log) hospital length-of-stay (P=0.04) and age (P=0.001). Approximately 1 in 10 ICU patients were frail and this frequency increased with age. The DCFS was associated with patient age and comorbidities and potentially predicts increased hospital length-of-stay but not other outcomes. Strategies to improve compliance with DCFS completion are needed.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".