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Record W2034082622 · doi:10.1136/jclinpath-2013-201734

Hernia sacs: is histological examination necessary?

2013· article· en· W2034082622 on OpenAlexaff
Tao Wang, Rajkumar Vajpeyi

Bibliographic record

VenueJournal of Clinical Pathology · 2013
Typearticle
Languageen
FieldMedicine
TopicIntraperitoneal and Appendiceal Malignancies
Canadian institutionsUniversity Health NetworkUniversity of Toronto
Fundersnot available
KeywordsMedicineFemoral herniaHerniaAbdominal wallInguinal herniaInguinal canalSurgeryHistologyGeneral surgeryRadiologyPathology

Abstract

fetched live from OpenAlex

The hernia sac is a common surgical pathology specimen which can occasionally yield unexpected diagnoses. The College of American Pathologists recommends microscopic examination of abdominal hernias, but leaves submission of inguinal hernias for histology to the discretion of the pathologist. To validate this approach at a tertiary care centre, we retrospectively reviewed 1426 hernia sacs derived from inguinal, femoral and abdominal wall hernias. The majority of pathologies noted were known to the clinician, including herniated bowel, lipomas and omentum. A malignancy was noted in three of 800 inguinal hernias and seven of 576 abdominal wall hernias; five of these lesions were not seen on gross examination. Other interesting findings in hernia sacs included appendices, endometriosis, a perivascular epithelioid cell tumour, and pseudomyxoma peritoneii. All hernia sacs should be examined grossly as most pathologies are grossly visible. The decision to submit inguinal hernias for histology may be left to the discretion of the pathologist, but abdominal and femoral hernias should be submitted for histology.

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)0.009
metaresearch head score (Gemma)0.034
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 score0.009
Threshold uncertainty score0.047

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.034
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.002
Science and technology studies0.0010.004
Scholarly communication0.0020.004
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0020.002

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 head0.067
GPT teacher head0.382
Teacher spread0.315 · 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

Citations41
Published2013
Admission routes1
Has abstractyes

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