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Record W2053472973 · doi:10.5489/cuaj.2341

On-table urethral catheterisation during laparoscopic appendicectomy: Is it necessary?

2015· article· en· W2053472973 on OpenAlexvenueno aff
Gregory J. Nason, Sher N. Baig, Matthew J. Burke, Asadullah Aslam, Michael E. Kelly, Leon Walsh, Hugh D. Flood, Subhasis Giri

Bibliographic record

VenueCanadian Urological Association Journal · 2015
Typearticle
Languageen
FieldMedicine
TopicMinimally Invasive Surgical Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineTable (database)General surgeryComputer scienceDatabase

Abstract

fetched live from OpenAlex

INTRODUCTION: Laparoscopic appendicectomy (LA) is the most commonly performed surgical emergency procedure. The aim of this study was to highlight a series of iatrogenic bladder injuries during LA and suggest a simple method of prevention. METHODS: A retrospective review was carried out of all LA performed in a university teaching hospital over a two year period 2012-2013. Iatrogenic visceral injuries were identified and operative notes examined. RESULTS: During the study period 1124 appendicectomies were performed. Four iatrogenic bladder injuries occurred related to secondary trocar insertion. No patient was catheterised preoperatively. One of the injuries was identified intra-operatively, another in the early postoperative period where as two re-presented acutely unwell post-discharge from hospital. Three were repaired by laparotomy and one laparoscopically. CONCLUSION: Iatrogenic secondary trocar induced bladder injuries are a rare but preventable and potentially serious complication of LA. Urethral catheterisation during LA is a safe and simple method which can prevent this complication.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.036
GPT teacher head0.281
Teacher spread0.245 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations9
Published2015
Admission routes1
Has abstractyes

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