Ultrasound-guided transversus abdominis plane block in obese cats: a preliminary cadaveric study
Bibliographic record
Abstract
ObjectivesThe aim of the present study was to investigate the distribution of adipose tissue in the abdominal wall of obese cats and compare the injectate spread and spinal nerve staining after ultrasound-guided transversus abdominis plane (TAP) block using lean (LBW) vs actual body weight (ABW).MethodsFour cat cadavers with a body condition score ⩾8/9 were included. Cat 1 was dissected to identify the TAP and describe abdominal fat distribution. Cats 2 and 3 received a two-point ultrasound-guided TAP injection of 0.25 ml/kg/point based on LBW and ABW, respectively. In cat 4, both hemiabdomens were randomly injected with the two volumes. Subsequent anatomic dissection assessed injectate distribution and the number of thoracic (T) and lumbar (L) spinal nerves stained ⩾1 cm circumferentially.ResultsThe mean weight of the cats was 7.5 ± 0.3 kg and they had a body condition score of 9/9. A thick layer of hypoechoic adipose tissue was observed ventral to the costal arch, between the rectus and transversus abdominis muscles, and a second thinner layer between the obliquus internus and transversus abdominis muscles. After crossing the adipose tissue, the ventral branches of spinal nerves lie in the fascial plane, superficial to the transversus abdominis muscle. LBW- and ABW-based injectate volumes stained the ventral branches from T12 to L1 and T11 to L1, respectively.Conclusions and relevanceTwo separate layers of adipose tissues are localized superficially to the transversus abdominis muscle in obese cats. Identifying the transversus abdominis muscle and adipose layers is crucial for the success of the TAP block. Injectate volumes based on ABW may provide wider staining of thoracolumbar spinal nerves than LBW. Further randomized clinical trials are needed in obese cats using locoregional anesthesia.
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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) | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".