Evaluation of psychomotor/motor disturbances in elderly medical inpatients
Bibliographic record
Abstract
Introduction Traditionally psychomotor subtypes have been investigated in patients with delirium in different settings and it has been found that those with hypoactive type is the largest proportion, often missed and with the worst outcomes. Aims and objectives We examined the psychomotor subtypes in an older age inpatients population, the effects that observed clinical variables have on psychomotor subtypes and their association with one year mortality. Methods Prospective study. Participants were assessed using the scales CAM, APACHE II, MoCA, Barthel Index and DRS-R98. Pre-existing dementia was diagnosed according to DSM-IV criteria. Psychomotor subtypes were evaluated using the two relevant items of DRS-R98. Mortality rates were investigated one year after admission day. Results The sample consisted of 200 participants [mean age 81.1 ± 6.5; 50% female; pre-existing cognitive impairment in 126 (63%)]. Thirty-four (17%) were identified with delirium (CAM+). Motor subtypes of the entire sample was: none: 119 (59.5%), hypo: 37 (18.5%), mixed: 15 (7.5%) and hyper: 29 (14.5%). Hypoactive and mixed subtype were significantly more frequent to delirious patients than to those without delirium, and none subtype more often to those without delirium. There was no difference in the hyperactive subtype between those with and without delirium. Hypoactive subtype was significant associated with delirium and lower scores in MoCA (cognition), while mixed was associated mainly with delirium. Predictors for one-year mortality were lower MoCA scores and severity of illness. Conclusions Psychomotor disturbances are not unique to delirium. Hypoactivity, this “silent epidemic” is also part of a deteriorated cognition. Disclosure of interest The authors have not supplied their declaration of competing interest.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".