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Record W4303579324 · doi:10.3390/ani12192674

An Anatomical, Sonographic, and Computed Tomography Study of the Transversus Abdominis Plane Block in Cat Cadavers

2022· article· en· W4303579324 on OpenAlexafffund
Marta Garbin, Sabrine Marangoni, Cyrielle Finck, Paulo V. Steagall

Bibliographic record

VenueAnimals · 2022
Typearticle
Languageen
FieldMedicine
TopicAnesthesia and Pain Management
Canadian institutionsUniversité de Montréal
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsTap waterMedicineCadaverAnatomyNuclear medicine

Abstract

fetched live from OpenAlex

This study compared the distribution of a bupivacaine–iopamidol–dye solution following ultrasound-guided in-plane TAP injection using a 1-point (TAP-L) or 2-point (TAP-SL) approach in cat cadavers. Two cadavers were used to study the TAP sonoanatomy while eight cadavers were enrolled in a randomized, prospective, blinded investigation. Each cat randomly received a TAP-L with 0.5 mL/kg in one hemiabdomen and a TAP-SL with 0.25 mL/kg/point in the contralateral hemiabdomen. After injection, computed tomography and dissection were performed to assess contrast distribution and number of stained target nerves. TAP-SL resulted in a wider contrast spread (mm) compared with TAP-L (87 ± 7 versus 71 ± 9; p = 0.002). The prevalence of nerve staining was higher using TAP-SL than TAP-L (p = 0.001). The ventral branches of T10, T11, T12, T13, L1 and L2 were stained in 2/8, 2/8, 5/8, 7/8, 4/8 and 1/8, and in 7/8, 7/8, 8/8, 8/8, 8/8 and 1/8 using TAP-L and TAP-SL approaches, respectively. Computed tomography and dissection identified minimal injectate intraperitoneally or within the falciform ligament fat following 1 TAP-L and 2 TAP-SL. Ultrasound-guided TAP-SL provided better injectate distribution around the thoracolumbar spinal nerve branches than TAP-L.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.312

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.014
GPT teacher head0.252
Teacher spread0.238 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations13
Published2022
Admission routes2
Has abstractyes

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