Cadaveric investigation of the minimum effective volume for ultrasound-guided suprainguinal fascia iliaca block
Bibliographic record
Abstract
Background This cadaveric dose-finding study investigated the minimum effective volume of dye in 90% of cases (MEV90), required to stain the femoral, lateral femoral cutaneous and obturator nerves for ultrasound-guided suprainguinal fascia iliaca block (SIFIB). Methods In cadaveric specimens of the lower limb, the block needle was advanced, medial to the anterosuperior iliac spine, until its tip was positioned between the internal oblique and iliacus muscles underneath the fascia iliaca. The dye was injected inside the fascia iliaca compartment. Volume assignment was carried out using a biased coin design, whereby the volume of dye administered to each cadaveric specimen depended on the response of the previous one. In case of failure, the next one received a higher volume (defined as the previous volume with an increment of 2.5 mL). If the previous cadaveric specimen had a successful block, the next one was randomized to a lower volume (defined as the previous volume with a decrement of 2.5 mL), with a probability of b =0.11, or the same volume, with a probability of 1– b =0.89. Success was defined as the staining of the femoral, lateral femoral cutaneous, and obturator nerves on dissection. Results Thirty-six cadavers (60 cadaveric specimens) were included in the study. Using isotonic regression and bootstrap CI, the MEV90 for ultrasound-guided SIFIB was estimated to be 62.5 mL (95% CI 60 to 65). Conclusion For ultrasound-guided SIFIB, the MEV90 of dye required to stain the femoral, lateral femoral cutaneous and obturator nerves is 62.5 mL. Further studies are required to correlate this finding with the MEV90 of local anesthetic in human subjects.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".