Clinical and Demographic Characteristics Associated With Postural Instability in Patients With Schizophrenia
Bibliographic record
Abstract
As people with schizophrenia grow older, prevention of falls in this older population has become a public health priority. It is therefore critically important to identify risk factors to effectively prevent falls. For this purpose, the degree of postural sway can serve as a convenient index of risk assessment. The objective of this study was to find clinical and demographic characteristics associated with postural instability. Inpatients and outpatients with schizophrenia or related psychosis were recruited at 2 hospitals in Japan. The clinical stabilometric platform, which measured a range of the trunk motion, and extrapyramidal side effects were evaluated between 9 and 11 A.M. Four hundred two subjects were enrolled (age: mean, 55.5 [SD, 14.4] years). A univariate general linear model showed that the use of antipsychotic drugs with a chlorpromazine equivalent of 10 or greater, being overweight, and inpatient treatment setting were associated with a greater degree of the range of postural sway. Another general linear model, including a subgroup of 300 subjects who did not present any extrapyramidal side effects, not only consolidated these findings, but also revealed a great degree of postural sway in older subjects. In addition, quetiapine was found to be associated with a greater range of postural sway among atypical antipsychotics. Schizophrenia patients generally showed a greater degree of postural instability, compared with the reference data of healthy people. These findings highlight truncal instability as a risk factor of falls in patients with schizophrenia, especially when they are overweight, old, and/or receiving antipsychotics with a chlorpromazine equivalent of 10 or greater, including quetiapine.
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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 | 0.000 | 0.001 |
| 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 | 0.000 | 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 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".