Incomplete functional recovery after delirium in elderly people: a prospective cohort study
Bibliographic record
Abstract
BACKGROUND: Delirium often has a poor outcome, but why some people have incomplete recovery is not well understood. Our objective was to identify factors associated with short-term (by discharge) and long-term (by 6 month) incomplete recovery of function following delirium. METHODS: In a prospective cohort study of elderly patients with delirium seen by geriatric medicine services, function was assessed at baseline, at hospital discharge and at six months. RESULTS: Of 77 patients, vital and functional status at 6 months was known for 71, of whom 21 (30%) had died. Incomplete functional recovery, defined as > or =10 point decline in the Barthel Index, compared to pre-morbid status, was present in 27 (54%) of the 50 survivors. Factors associated with death or loss of function at hospital discharge were frailty, absence of agitation (hypoactive delirium), a cardiac cause and poor recognition of delirium by the treating service. Frailty, causes other than medications, and poor recognition of delirium by the treating service were associated with death or poor functional recovery at 6 months. CONCLUSION: Pre-existing frailty, cardiac cause of delirium, and poor early recognition by treating physicians are associated with worse outcomes. Many physicians view the adverse outcomes of delirium as intractable. While in some measure this might be true, more skilled care is a potential remedy within their grasp.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".