Efficient Imaging: Examining the Value of Ultrasound in the Diagnosis of Traumatic Adult Brachial Plexus Injuries, A Systematic Review
Bibliographic record
Abstract
BACKGROUND: Traumatic brachial plexus injury (BPI) can result in debilitating sequelae of the upper extremity. Presently, therapeutic decisions are based on the mechanism of injury, serial physical examination, electromyography, nerve conduction, and imaging studies. While magnetic resonance imaging is the current imaging modality of choice for BPI, ultrasound is a promising newcomer that is inexpensive, accessible, and available at point of care. OBJECTIVE: To evaluate ultrasound as a diagnostic tool in the assessment of traumatic adult BPI through a systematic review. METHODS: An electronic literature search was completed in MEDLINE, EMBASE, CINAHL, and Cochrane databases up to July 2016. Two independent reviewers completed the screening and data extraction. Methodological quality of studies was evaluated using the Quality Assessment of Diagnostic Accuracy Studies-2 tool. Statistical analysis was used to estimate pooled sensitivities and study heterogeneity. RESULTS: Seven studies were included. Four studies compared the detection of pre- and postganglionic lesions at different levels (C5-T1) to surgical exploration as the reference standard. Sensitivity of lesion detection was greater in the upper and middle spinal nerves: C5 (93%, confidence interval [CI] = 78%-100%), C6 (94%, CI = 82%-100%), and C7 (95%, CI = 86%-100%) than in the lower: C8 (71%, CI = 36%-95%) and T1 (56%, CI = 29%-81%). CONCLUSION: Individual studies demonstrate ultrasound as an effective diagnostic tool for traumatic adult BPI. Sensitivity of lesion detection was noted to be greater in the upper and middle (C5-C7) than in the lower spinal nerves (C8, T1). Further standardized studies should be performed to confirm the utility of ultrasound in the diagnosis of traumatic adult BPI.
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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 | 0.018 | 0.108 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.011 | 0.010 |
| Bibliometrics | 0.020 | 0.016 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".