Pathways for Diagnosis and Multimodal Management, Including Botulinum Neurotoxin Therapy, in Shoulder Conditions Following Acquired Central Nervous System Lesions
Bibliographic record
Abstract
There is limited published guidance available to help less experienced practitioners assess and manage shoulder conditions, including spasticity, after acquired central nervous system (CNS) lesions. To address this gap, 11 spasticity and dystonia experts convened in a 2023 meeting to build on existing guidance, provide consensus on best treatment practice, and develop expert recommendations to guide the diagnosis and treatment of complications of shoulder conditions following CNS lesions. Presentations by each expert on diagnosis and management were followed by discussion; consensus on assessment and treatment practices was identified and recommendations developed. The expert panel recommended an assessment approach structured using the following components: patient history, including interpretation of reported symptoms; observation of postures and pain responses; clinical examination with targeted tests for specific signs; diagnostic tests; and assessment of upper limb impairment, activity limitations, and participation restrictions. This assessment process and the recommended measures recognize the importance of identifying shoulder involvement in upper limb spasticity as part of the diagnostic process in shoulder conditions following CNS lesions. These recommendations provide a practical approach to diagnosis and treatment for clinicians who are less experienced in evaluating and treating such conditions, simplifying otherwise complicated clinical scenarios.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".