Complications and short‐term outcomes associated with single‐port laparoscopic splenectomy in dogs
Bibliographic record
Abstract
OBJECTIVE: To describe a technique and report complications and outcome for single-port laparoscopic splenectomy in dogs. STUDY DESIGN: Retrospective study. ANIMALS: Twenty-two client-owned dogs. METHODS: Medical records of dogs that underwent single-port laparoscopic splenectomy at 4 veterinary teaching hospitals were evaluated. Commercially available single-port devices were used in all dogs. In all cases, a vessel-sealing device was used to perform a hilar splenectomy. After the procedure was completed, the spleen was exteriorized through the single-port device incision or placed into a specimen retrieval device; enlargement of the incision was required in some cases. RESULTS: Median weight of dogs was 9.9 kg (interquartile range [IQR], 7.0-26.0). Splenectomy was performed because of splenic mass (n = 14), diffuse splenic disease (n = 4), or as adjunctive treatment for management of immune-mediated disease (n = 4). In cases with splenic masses, median maximal diameter of the largest splenic mass was 2.0 cm (IQR, 1.3-2.5). In 6 of 22 cases, mild splenic capsular bleeding occurred during the procedure. Conversion occurred in 6 of 22 cases to either a laparoscopic-assisted approach (n = 5) or an open celiotomy (n = 1). Reasons for conversion included large splenic dimensions (n = 3), adhesion formation (n = 1) or poor visualization resulting from abundant intra-abdominal fat (n = 1) or hemorrhage (n = 1). Heavier body weight was significantly associated with conversion (odds ratio, 1.62; 95% confidence interval, 1.05-2.51), but body condition score, having a splenic mass, splenic mass size, and surgical time were not. CONCLUSION: Single-port laparoscopic splenectomy is an effective approach for elective splenectomy in dogs. The technique may be well suited to smaller dogs with modestly sized splenic masses or diffuse splenic disease.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".