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Record W4409696844 · doi:10.1055/s-0045-1807749

Prophylactic Operative Interventions for Preventing Parastomal Hernias after Colorectal Surgery

2025· article· en· W4409696844 on OpenAlexaff
Tyler McKechnie, Neil Smart, Cagla Eskicioglu

Bibliographic record

VenueClinics in Colon and Rectal Surgery · 2025
Typearticle
Languageen
FieldMedicine
TopicStoma care and complications
Canadian institutionsMcMaster UniversityImpact
Fundersnot available
KeywordsMedicineColorectal surgerySurgeryPsychological interventionGeneral surgeryAbdominal surgeryNursing

Abstract

fetched live from OpenAlex

The most common long-term stoma-related morbidity following colorectal surgery is parastomal hernia formation. Given the risk of developing parastomal hernias and the risk of postoperative complications following their repair, practices have evolved to incorporate prophylactic strategies to reduce the risk of parastomal hernia formation after colorectal surgery. The majority of the data forming the evidence base for parastomal hernia prophylaxis pertains to patients undergoing end colostomy formation in the setting of colorectal cancer. The only prophylactic intervention for prevention of parastomal hernia formation with substantial amounts of high-quality data is the insertion of prophylactic mesh at the index operation for patients undergoing formation of a permanent end colostomy. Other interventions that have been proposed but have less published data substantiating their use include lateral pararectus stoma placement, extraperitoneal stoma creation, circular stoma trephine, and small fascial defects. This chapter will review each of these interventions in detail, along with the associated literature supporting or refuting their use. Additionally, we will discuss other important issues regarding the evidence base for parastomal hernia prophylaxis, parastomal hernia classifications, and risk factors for developing parastomal hernias.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.069
Threshold uncertainty score0.492

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.060
GPT teacher head0.406
Teacher spread0.346 · 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.

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

Citations0
Published2025
Admission routes1
Has abstractyes

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