The Role of Intraoperative Frozen Section Histology in Obstetrical Brachial Plexus Reconstruction
Bibliographic record
Abstract
The use of frozen section histological analysis in primary obstetrical brachial plexus palsy reconstruction, though widespread, is not universally practiced. Our objective was to develop a histological grading scale that could be used to determine whether further resection of a microscopically suboptimal, though grossly satisfactory stump could lead to a measurable improvement in histological appearance. A 13-point grading tool assessing attributes of the epineurium, perineurium, and endoneurium was tested for interrater reliability. The histological appearance of initial nerve biopsies and of subsequent nerve reexcisions stained with toluidine blue was reviewed retrospectively (n = 52). Specimens were graded in a blinded fashion by a neuropathologist and a medical student. There was high agreement between expert and novice global rating scores with an intraclass correlation coefficient of 0.89 (95% confidence interval 0.85 to 0.93). A comparison of scores between subsequent sections of the same nerve stump revealed a significant decrease of 3.00 (expert) and 2.00 (novice) points ( P < 0.001) in the median global rating score, demonstrating improvement in histological grade. The novel grading tool was used to demonstrate that recutting a microscopically poor, though grossly acceptable nerve stump in obstetrical palsy surgery can yield a significantly improved histological grade.
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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 | 0.019 | 0.030 |
| 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 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".