Can the Sup-ER Protocol Decrease the Prevalence and Severity of Elbow Flexion Deformity in Brachial Plexus Birth Injuries?
Bibliographic record
Abstract
Background: Brachial plexus birth injuries (BPBIs) can often result in functional and cosmetic deficits including, according to a recent scoping review, elbow flexion contractures in up to 48%. A treatment algorithm that includes a custom long-arm orthosis to optimize early glenohumeral joint positioning (Sup-ER protocol) has been shown to improve shoulder range of motion. Although the protocol was not intentionally designed to affect the elbow, this study investigates the prevalence and severity of elbow flexion contractures in children treated with that protocol. Methods: This prospective cross-sectional cohort study examined 16 children aged 4 and older with BPBI severe enough to be treated with the Sup-ER protocol. Passive and active elbow flexion and extension range of motion (ROM) were assessed in both arms. Elbow flexion contractures were defined as > 5 o from neutral. Results: Within the cohort of 16 patients (mean age: 7.0 years, range: 4.5-11.6 years), the mean maximal passive elbow extension was -6.2° in the affected arm and + 5.1° (hyperextension) in the unaffected arm. Zero patients had a severe elbow flexion contracture (>30 o ) and only 6/16 met the lowest threshold definition of elbow flexion contracture (>5 o ), with a mean onset at 22 months of age. Conclusions: This study suggests an unintended decreased prevalence and severity of elbow flexion contractures in children with more severe BPBI treated with the Sup-ER protocol, relative to published values.
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".