Workshop (Clinical/Best Practice Implementation) ID 1983439
Bibliographic record
Abstract
Objective Participants of this workshop will be able to identify and recommend multimodal treatment options in managing challenging cases in spasticity management of lower limbs in spinal cord injury (SCI). Methods The complexities of spasticity management will be identified through case-based presentations, including assessment and treatment directed by patient-oriented goals. Facilitated interactive discussion on these cases will engage participants for input, debate and critique. Procedural techniques for neurolysis will be discussed. Results Goals of spasticity management in SCI often include both proximal and distal lower limb problems. Therapies include oral medications, chemodenervation with neuromuscular junction blockade with botulinum toxin or neurolysis with phenol, intrathecal baclofen, bracing, and surgery. Many patients require multiple of these therapeutic options during their course of spasticity management. Discussion Multimodal therapy as part of the clinician toolbox is essential in tone management. Patient-oriented goals are important guideposts in spasticity management. SCI is a lifespan condition, and often patient goals and health conditions change over the years, requiring different tools to address such longitudinal needs.
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 | 0.011 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.635 | 0.278 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".