Minimally invasive small intestinal exploration and targeted abdominal organ biopsy with a wound retraction device in 42 cats (2005‐2015)
Bibliographic record
Abstract
OBJECTIVE: To describe the surgical technique and evaluate short-term outcome after minimally invasive small intestinal exploration and targeted organ biopsy with a wound retractor device (WRD) in cats. STUDY DESIGN: Multi-institutional retrospective study. ANIMALS: Forty-two cats. METHODS: A wound retractor was inserted into the abdomen on the ventral midline through a 2-4 cm incision at the level of the umbilicus. Short segments (6-10 cm long) of intestinal tract were sequentially exteriorized and explored through the WRD. Full thickness, small intestinal biopsies were obtained extracorporeally via the WRD. A commercially available single-port device was inserted through the WRD for laparoscopic exploration of the abdomen. RESULTS: The majority of the small intestine could be exteriorized and explored through the WRD. In all cases, full thickness biopsies of the small intestine of diagnostic quality were obtained. The most common histological findings were inflammatory bowel disease (n = 16), intestinal lymphoma (n = 14), and eosinophilic enteritis (n = 7). Two cases required conversion to a traditional open laparotomy due to abdominal pathology diagnosed after placement of the WRD (abdominal adhesions and need for a splenectomy). Postoperative complications occurred in 4 of 39 cats (10.3%), leading to 2 deaths after discharge from the hospital. CONCLUSIONS AND CLINICAL RELEVANCE: MISIETB with a WRD alone or combined with laparoscopy is a safe technique for small intestinal exploration and targeted abdominal organ biopsy in cats. Single-port laparoscopy can effectively be performed through the WRD for complete abdominal exploration and biopsy of abdominal organs.
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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 | low |
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) | 0.000 | 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 | 0.001 |
| Research integrity | 0.001 | 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, 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".