The PRaCTICaL study of nurse led, intensive care follow-up programmes for improving long term outcomes from critical illness: a pragmatic randomised controlled trial
Bibliographic record
Abstract
OBJECTIVES: To test the hypothesis that nurse led follow-up programmes are effective and cost effective in improving quality of life after discharge from intensive care. DESIGN: A pragmatic, non-blinded, multicentre, randomised controlled trial. SETTING: Three UK hospitals (two teaching hospitals and one district general hospital). PARTICIPANTS: 286 patients aged >or=18 years were recruited after discharge from intensive care between September 2006 and October 2007. INTERVENTION: Nurse led intensive care follow-up programmes versus standard care. Main outcome measure(s) Health related quality of life (measured with the SF-36 questionnaire) at 12 months after randomisation. A cost effectiveness analysis was also performed. RESULTS: 286 patients were recruited and 192 completed one year follow-up. At 12 months, there was no evidence of a difference in the SF-36 physical component score (mean 42.0 (SD 10.6) v 40.8 (SD 11.9), effect size 1.1 (95% CI -1.9 to 4.2), P=0.46) or the SF-36 mental component score (effect size 0.4 (-3.0 to 3.7), P=0.83). There were no statistically significant differences in secondary outcomes or subgroup analyses. Follow-up programmes were significantly more costly than standard care and are unlikely to be considered cost effective. CONCLUSIONS: A nurse led intensive care follow-up programme showed no evidence of being effective or cost effective in improving patients' quality of life in the year after discharge from intensive care. Further work should focus on the roles of early physical rehabilitation, delirium, cognitive dysfunction, and relatives in recovery from critical illness. Intensive care units should review their follow-up programmes in light of these results. TRIAL REGISTRATION: ISRCTN 24294750.
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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 | 0.018 | 0.046 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".