Stability of Postoperative Delirium Psychomotor Subtypes in Individuals with Hip Fracture
Bibliographic record
Abstract
OBJECTIVES: To determine the stability of psychomotor subtypes of delirium over time and identify characteristics associated with delirium psychomotor subtypes in individuals undergoing surgical repair of hip fracture. DESIGN: Prospective cohort study. SETTING: The Transfusion Trigger Trial for Functional Outcomes in Cardiovascular Patients Undergoing Surgical Hip Fracture Repair Cognitive Ancillary Study was conducted at 13 participating sites from 2008 to 2009. PARTICIPANTS: Individuals who had undergone surgical repair of hip fracture (N=139). MEASUREMENTS: Delirium was assessed up to four times postoperatively using the Confusion Assessment Method (CAM) and the Memorial Delirium Assessment Scale. Psychomotor subtypes of delirium were categorized as hypoactive, hyperactive, mixed, and normal psychomotor activity. RESULTS: Incidence of postoperative delirium was 41% (n=57). Of 90 CAM-positive (CAM+) observations, 56% were hypoactive, 10% were hyperactive, 21% were mixed, and 14% had normal psychomotor symptoms. Of 26 participants with more than one CAM+ assessment, 50% maintained subtype stability over time. Participants with hypoactive or normal psychomotor symptoms (n=31) were less likely to have chart documentation of delirium than participants with any hyperactive symptoms (n=19) (22% vs 58%, P=.009). CONCLUSION: Psychomotor subtypes of delirium often fluctuate from assessment to assessment, rather than representing fixed categories of delirium. Hypoactive delirium is the most common presentation of delirium but is the least likely to be documented by healthcare providers.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".