Evaluation of minimally invasive small intestinal exploration and targeted abdominal organ biopsy with use of a wound retraction device in dogs: 27 cases (2010–2017)
Bibliographic record
Abstract
OBJECTIVE: To describe surgical technique, biopsy sample quality, and short-term outcome of minimally invasive small intestinal exploration and targeted abdominal organ biopsy (MISIETB) with use of a wound retraction device (WRD) in dogs. ANIMALS: 27 client-owned dogs that underwent MISIETB with a WRD at 1 of 4 academic veterinary hospitals between January 1, 2010, and May 1, 2017. PROCEDURES: Medical records were retrospectively reviewed, and data collected included signalment; medical history; findings from physical, ultrasonographic, laparoscopic, cytologic, and histologic evaluations; surgical indications, procedures, duration, and complications; and short-term (14-day) outcomes. The Shapiro-Wilk test was used to evaluate the normality of continuous variables, and descriptive statistics were calculated for numeric variables. RESULTS: Laparoscopic exploration was performed through a multicannulated single port (n = 18), multiple ports (5), or a single 6-mm cannula (4). Median length of the incision for WRD placement was 4 cm (interquartile [25th to 75th percentile] range, 3 to 6 cm). All biopsy samples obtained had sufficient diagnostic quality. The 2 most common histologic diagnoses were lymphoplasmacytic enteritis (n = 14) and intestinal lymphoma (5). Twenty-five of 27 (93%) dogs survived to hospital discharge, and 3 (12%) dogs had postsurgical abnormalities unrelated to surgical technique. CONCLUSIONS AND CLINICAL RELEVANCE: Results indicated that MISIETB with WRD was an effective method for obtaining diagnostic biopsy samples of the stomach, small intestine, pancreas, liver, and mesenteric lymph nodes in dogs. Prospective comparison between MISIETB with WRD and traditional laparotomy for abdominal organ biopsy in dogs is warranted.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".