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Record W3179294590 · doi:10.1111/vsu.13675

Outcomes after transperitoneal laparoscopic ureteronephrectomy for the treatment of primary renal neoplasia in seven dogs

2021· article· en· W3179294590 on OpenAlexaff
Edward J. Hart, Ameet Singh, Chris Thomson, Ryan Appleby, Danielle Richardson, Samuel E. Hocker, Sarah Bernard, Christopher J. Pinard

Bibliographic record

VenueVeterinary Surgery · 2021
Typearticle
Languageen
FieldVeterinary
TopicVeterinary Medicine and Surgery
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsMedicinePerioperativeInterquartile rangeLaparotomySurgeryLaparoscopy

Abstract

fetched live from OpenAlex

OBJECTIVE: To describe the perioperative characteristics and outcomes in dogs that underwent transperitoneal laparoscopic ureteronephrectomy (TLU) for primary renal neoplasia. STUDY DESIGN: Short case series. ANIMALS: Seven client-owned dogs. METHODS: Medical records were reviewed and data extracted regarding perioperative characteristics and animal outcomes. TLU was performed using a single-port + 1 or multiple port techniques. Hemostatic clips or a vessel-sealing device were used for occlusion of renal hilar vessels. The ureter was occluded and transected close to the ureterovesicular junction and the tumor was placed in a specimen retrieval bag for extraction from the abdomen. RESULTS: (interquartile range [IQR] 14.76-94.85). Median surgery time for TLU was 90 minutes (IQR 85-105). In one dog, elective conversion to open laparotomy was performed due to large tumor size. Median time to discharge was 31 hours (IQR 24-48). No major perioperative complications occurred and all dogs survived to discharge. Progression free survival in four dogs was 422 days (IQR 119-784). CONCLUSION: TLU was performed for the extirpation of modest sized primary renal tumors with acceptable perioperative outcomes and a low complication rate. CLINICAL RELEVANCE: TLU may be considered for the treatment of selected cases of primary renal neoplasia in dogs.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.138
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
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 head0.081
GPT teacher head0.319
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.

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

Citations1
Published2021
Admission routes1
Has abstractyes

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