Module 2
Bibliographic record
Abstract
Spasticity management should be part of a well-coordinated and comprehensive rehabilitation program that is patient-centric and goal-specific. There are a variety of options available for the treatment of spasticity. A usual approach is starting with the least invasive treatment modalities initially and gradually increasing to more complex interventions as this is required. This curriculum considers oral antispasticity drugs in terms of mechanism of action, clinical use, efficacy, and adverse events. It also presents other treatment options, such as chemical neurolysis using phenol and alcohol and chemodenervation using botulinum toxin A (BoNT-A). Therapeutic intramuscular injections of BoNT-A require sound patient selection, accurate muscle selection, and precise localization. The common methods for achieving these are described. The importance of physiotherapy is explained, along with the necessity to combine treatment modalities to address spasticity and the various components of the upper motor neuron syndrome. Recognizing differences in various health-care systems across countries and regions, the authors aim to present various treatment options. While this section of the curriculum highlights the importance of an interdisciplinary effort in managing spasticity, it is understandable that not all treatment options are available uniformly. The challenge to clinicians is to make the most of the management options on hand to optimize outcomes.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.663 | 0.381 |
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".