The impact of psychomotor subtypes and duration of delirium on 6‐month mortality in hip‐fractured elderly patients
Bibliographic record
Abstract
OBJECTIVE: Studies exploring the incidence and impact of the psychomotor subtypes of postoperative delirium (POD) on the survival of hip fracture patients are few, and results are inconsistent. We sought to assess the incidence of POD subtypes and their impact, in addition to delirium duration, on 6-month mortality in older patients after hip-fracture surgery. METHODS: This is a prospective study involving 571 individuals admitted to an Orthogeriatric Unit within a 5-year period with a diagnosis of hip fracture. Survival status was assessed 6 months after posthip fracture surgery. Postoperative delirium was diagnosed using the Diagnostic and Statistical Manual of Mental Disorders. Postoperative delirium subtypes were classified according to Lipowski's criteria. Cox regressions were used to evaluate the associations between POD subtypes, POD duration, and 6-month mortality, adjusting for covariates. RESULTS: The incidence of psychomotor POD subtypes was hypoactive 57 (10.0%), hyperactive 84 (14.7%), and mixed 79 (13.8%). Six-month mortality rates were 8.3%, 10.7%, 36.8%, and 29.1% in the no-delirium, hyperactive, hypoactive, and mixed-delirium subgroups, respectively. In adjusted models, the hypoactive subgroup (Hazard Ratio, HR = 3.14, 95% Confidence Intervals, CI, 1.63-6.04) and mixed subgroup (HR = 2.89, 95% CI, 1.49-5.62) showed high mortality rates and a significantly increased risk of mortality associated with POD duration as well. CONCLUSIONS: Hyperactive delirium was the most common POD psychomotor subtype, but hypoactive and mixed POD were associated with 6-month mortality risk. Moreover, the risk of death 6 months after surgery increased for both subgroups (hypoactive and mixed) with increasing duration of POD.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".