How Institution of the Sup-ER Protocol in a Clinic Changed Procedure Patterns in Upper Brachial Plexus (Erb’s Type) Birth Injuries
Bibliographic record
Abstract
BACKGROUND: For children with upper brachial plexus birth injury (BPBI; C5, C6, ±C7 roots), most clinics first recommend nonsurgical treatment followed by primary and/or secondary surgical interventions in selected patients. Since 2008, we have used an infant shoulder repositioning protocol (supination-external rotation [Sup-ER]) designed to prevent shoulder internal rotation contracture and its potential effects on the shoulder joint. This study characterizes our clinic's current choice, number, and timing of primary and secondary procedural interventions (including Botox) and compares Sup-ER protocol patients with those of our historical controls. METHODS: The records of all patients with upper BPBI who underwent procedures from 2001 to 2018 were retrospectively reviewed and grouped into a historical (2001-2007, n = 20) and recent (2008-2018, n = 23) cohort. Patient demographics, procedure types and timing, and functional outcomes were collected and analyzed. RESULTS: Since the 2008 institution of the Sup-ER protocol, fewer brachial plexus exploration and grafting (BPEG) surgeries were performed and none in later infancy, where nerve transfers were preferred. There were more and earlier Botox injections. There were fewer tendon transfers, and the preoperative indications were from a higher level of function. CONCLUSIONS: We now see fewer indications for BPEG surgeries overall. After the 3-month-age group, more direct nerve transfers are indicated instead of the BPEG surgery if nerve surgery is required at all. Shoulder tendon transfer rates have decreased. Humeral osteotomies are not seen in our recent group. Glenoid osteotomies within tendon transfers are rare in both groups.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".