Non‐Sedated Rapid Volumetric Proton Density MRI Predicts Neonatal Brachial Plexus Birth Palsy Functional Outcome
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: The current prognostic biomarker of functional outcome in brachial plexus birth palsy is serial clinical examination throughout the first 6 months of age. This can delay surgical treatment and prolong parental anxiety in neonates who will recover spontaneously. A potentially superior biomarker is a volumetric proton density MRI performed at clinical presentation and within the first 12 weeks of life, providing a high spatial and contrast resolution examination in 4 minutes. METHODS: Nine neonates ranging in age from 4 to 9 weeks who presented with brachial plexus birth palsy were enrolled. All subjects underwent non-sedated 3 Tesla MRI with Cube Proton Density MRI sequence at the same time as their initial clinical visit. Serial clinical examinations were conducted at routine 4 week intervals and the functional performance scores were recorded. MRI findings were divided into pre-ganglionic and post-ganglionic injuries and a radiological scoring system (Shriners Radiological Score) was developed for this study. RESULTS: Proton Density MRI was able to differentiate between pre-ganglionic and post-ganglionic injuries. Radiological scores (Shriners Radiological Score) correlated better with functional performance at 6 months of age (P = .022) than the initial clinical examinations (Active Movement Scale P = .213 and Toronto P = .320). CONCLUSIONS: Rapid non-sedated volumetric Cube Proton Density MRI protocol performed at initial clinical presentation can accurately grade severity of brachial plexus birth palsy injury and predict functional performance at 6 months of age.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".