Clinical Practice of Epidural Puncture in Dogs and Cats Assisted by a Commercial Acoustic Puncture Assist Device–Epidural Locator: Preliminary Results
Bibliographic record
Abstract
The objective of this study was to compare an Acoustic Puncture Assist Device-Epidural Locator (APAD-EL) with the "pop sensation" (POP) and "lack of resistance" (LOR) commonly used to confirm penetration of the ligamentum flavum and to ensure correct epidural placement in dogs and cats. We recruited 38 dogs and cats undergoing surgery and receiving epidural analgesia. Two anesthetists performed epidural puncture using the POP and LOR signs. Simultaneously, APAD-EL was used to collect visual and acoustic confirmation during advancement and placement of the needle tip for post hoc evaluation. A positive APAD-EL sign consists of a sudden pressure drop at the needle tip visible on a display and a concomitant pitch change of an acoustic signal. Failure to record a sudden pressure drop is considered a negative APAD sign. Descriptive statistics were used. In 32 patients with positive POP and LOR, the APAD was also positive. In one patient, POP was positive with a negative LOR and APAD result. Five patients had negative POP but positive LOR. Four patients had APAD positive and one (a dog) APAD negative. The study results showed that the APAD-EL information supports the subjective signs of correct needle placement suggested by positive POP and LOR experienced by trained anesthetists. The technique can be useful to assist difficult epidural puncture and as a training and teaching tool.
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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 | 0.007 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".