Use of the Posterior Lumbar Approach for Psoas Major Injection in Hip Flexor Spasticity
Bibliographic record
Abstract
BACKGROUND: Hip flexor spasticity in patients with upper motor neuron syndrome of multiple etiologies has been managed with botulinum neurotoxin injections mainly targeting the "iliopsoas" muscle. A lumbar approach to target psoas major (PM) has been described; however, it has not been incorporated into clinical practice due to perceived risk of injury to surrounding structures. This study will investigate the feasibility and accuracy of ultrasound-guided (UG)-PM injection using a lumbar approach by assessing the intra/extramuscular injectate spread in cadaveric specimens. METHODS: In eight lightly embalmed specimens, toluidine blue dye/saline was injected into PM using a UG-posterior lumbar approach. The posterior abdominal wall was exposed, and dye spread and surrounding structures digitized and modeled in 3D. The area and vertebral level of dye spread, distance of dye to the inferior vena cava (IVC), and abdominal aorta (AA) and dye spread to adjacent organs were quantified. RESULTS: The models enabled visualization of the dye spread in 3D. Mean area of dye spread was 24.4 ± 2.8 cm2; most commonly between L2 and L4 vertebral levels. Mean distance of the dye to AA was 3.2 ± 1.2 cm and to IVC was 1.8 ± 0.4 cm. Dye spread remained intramuscular in all but one specimen. No dye spread occurred to any adjacent organs. CONCLUSIONS: The injection of PM using the UG-posterior lumbar approach was consistent and without spread to surrounding structures. This technique alone or in addition to the anterior approach is expected to have better clinical outcomes in the treatment of hip flexor spasticity. Further clinical studies are required.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".