Botulinum toxin injection for hemiplegic shoulder pain: Do we know enough yet?
Bibliographic record
Abstract
In patients with spastic hemiplegia, botulinum toxin A (BoNTA) injection into the subscapularis (SB) has been shown to decrease shoulder pain and increase range of motion (Unlu et al., 2010; Yelnik et al., 2007). The intramuscular innervation pattern of SB has not been well studied, but could provide insight into neuromuscular partitioning, enabling the development of an optimal injection approach that enhances the efficacy of BoNTA. The purpose of this study is to document the extra‐ and intramuscular innervation patterns of SB throughout the muscle volume to identify neuromuscular partitions. Forty‐five formalin embalmed cadaveric specimens were used in this study. Extraand intramuscular innervation patterns of SB were documented using digitization and 3D modelling (n=7), and dissection and photography (n=38). Innervation patterns throughout the muscle volume were analyzed. The number of extramuscular branches (2–5) were correlated with the four identified intramuscular innervation patterns, partitioning the muscle into two parts (superior and inferior; n=11) or three parts (superior, middle and inferior; n=34). Due to the presence of neuromuscular partitions, injection in multiple locations may be required to achieve maximal effect. Future studies are needed to determine whether the number of partitions injected correlates with clinical outcomes.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".