Revisiting Physiologic and Psychologic Triggers that Increase Spasticity
Bibliographic record
Abstract
OBJECTIVE: The aims of this study were to systematically identify and summarize the literature examining the impact of physiologic and psychologic triggers on spasticity and discuss the evidence supporting various types of triggers. DESIGN: PubMed, EMBASE, CINAHL, and PEDro databases were searched using specific keyword combinations. Only studies using clinical tests or self-reports of spasticity were included. RESULTS: A total of 1152 articles were scanned for relevance, and of 44 relevant articles, 24 were reviewed. Pregnancy, posture, cold, circadian rhythm, and skin conditions increased spasticity and were measured using objective clinical tests. Self-reports of spasticity suggest that triggers such as bowel- and bladder-related issues, menstrual cycle, mental stress, and tight clothing can all increase spasticity. No literature evidence of increase in spasticity in response to heterotopic ossification, hemorrhoids, deep vein thrombosis, fever, and sleep patterns was found. CONCLUSIONS: Although self-reports indicate a strong possibility of increasing spasticity, without objective examination, the true effects of these triggers on spasticity remain inconclusive. Most studies reviewed here were performed in the spinal cord injury population; therefore, it is not known whether these triggers induce similar effects in persons with other neurologic etiologies.
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 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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".