Bibliographic record
Abstract
OBJECTIVE: To raise awareness of spasticity in primary care and clarify how to identify, diagnose, and manage it effectively and efficiently in patients with pre-existing neurologic conditions. SOURCES OF INFORMATION: . Other relevant guidelines and resources were reviewed and used. MAIN MESSAGE: Spasticity is a common secondary complication in conditions such as spinal cord injury, multiple sclerosis, stroke, cerebral palsy, and other neuromuscular physical disabilities and can have a negative effect on health and quality of life. Factors such as inconsistent definition, poorly understood mechanism, and relatively low prevalence make spasticity seem like a daunting condition to manage. Furthermore, its variable presentation and effect on a patient's quality of life, and its range of treatments with varying levels of evidence, can make treatment challenging in primary care and in other clinical settings. Family physicians play an important role in recognizing and inquiring about spasticity and its changes, triggers, and effects on function. Ruling out reversible causes is important. Many management strategies can be instituted by family physicians. CONCLUSION: Managing spasticity might be unfamiliar to many practitioners. It is important for physicians to understand spasticity and the potential treatment options available to improve quality of life. The current review provides concise information on the clinical relevance of spasticity in primary care and how to assess and manage it effectively and efficiently in those with chronic neurologic conditions.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".