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Record W6939331082 · doi:10.60692/d96fz-r8n43

2017 WSES guidelines for the management of iatrogenic colonoscopy perforation

2018· article· en· W6939331082 on OpenAlexaff

Bibliographic record

VenueGreater South Information System · 2018
Typearticle
Languageen
FieldMedicine
TopicColorectal Cancer Screening and Detection
Canadian institutionsFoothills Medical Centre
Fundersnot available
KeywordsPerforationColonoscopyComplicationMEDLINEEndoscopyIatrogenic injuryEmergency surgery

Abstract

fetched live from OpenAlex

Iatrogenic colonoscopy perforation (ICP) is a severe complication that can occur during both diagnostic and therapeutic procedures. Although 45-60% of ICPs are diagnosed by the endoscopist while performing the colonoscopy, many ICPs are not immediately recognized but are instead suspected on the basis of clinical signs and symptoms that occur after the endoscopic procedure. There are three main therapeutic options for ICPs: endoscopic repair, conservative therapy, and surgery. The therapeutic approach must vary based on the setting of the diagnosis (intra- or post-colonoscopy), the type of ICP, the characteristics and general status of the patient, the operator's level of experience, and surgical device availability. Although ICPs have been the focus of numerous publications, no guidelines have been created to standardize the management of ICPs. The aim of this article is to present the World Society of Emergency Surgery (WSES) guidelines for the management of ICP, which are intended to be used as a tool to promote global standards of care in case of ICP. These guidelines are not meant to substitute providers' clinical judgment for individual patients, and they may need to be modified based on the medical team's level of experience and the availability of local resources.

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)0.005
metaresearch head score (Gemma)0.016
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.009
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.016
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0050.002
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0020.002
Research integrity0.0040.004
Insufficient payload (model declined to judge)0.0080.007

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 head0.104
GPT teacher head0.315
Teacher spread0.212 · 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 designNot applicable
Domainnot available
GenreMethods

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
Published2018
Admission routes1
Has abstractyes

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