Predictive Value of Computed Tomographic Myelography in Obstetrical Brachial Plexus Palsy
Bibliographic record
Abstract
Preoperative radiologic studies to detect root avulsions of the brachial plexus caused by birth trauma are considered useful in assisting with surgical planning for reconstruction. In this study, the predictive value of computed tomographic (CT) myelography in detecting nerve root avulsions at our institution was determined. Sixty-three consecutive patients with an obstetrical brachial plexus palsy who had had both preoperative CT myelography and reconstructive surgery were selected. All CT myelograms were analyzed post hoc by a single neuroradiologist in a manner blind to the surgical findings. At each root level of the brachial plexus, the presence of a pseudomeningocele was noted along with the presence or absence of rootlets within each identified pseudomeningocele. Extraforaminal root avulsions later determined at surgery were reviewed by a single surgeon in a manner blind to the radiographic results. Surgical and radiographic findings were then compared at each corresponding root level. A total of 281 roots were examined. The sensitivity, specificity, positive predictive value, and likelihood ratio for root avulsions with pseudomeningoceles were 0.63, 0.85, 0.40, and 4.2, respectively. For pseudomeningoceles for which rootlets traversing the sac could not be identified, these values were 0.37, 0.98, 0.74, and 18.5, respectively. The presence of pseudomeningoceles with or without rootlets was not a sensitive indicator of root avulsions. Root avulsions were better predicted by identifying the absence of rootlets in a pseudomeningocele. This absence on CT myelography may be used to suggest an extraforaminal root avulsion due to its high specificity and high likelihood ratio.
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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 | 0.001 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".