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Record W4366335501 · doi:10.46747/cfp.6904257

Anterior cutaneous nerve entrapment syndrome in children

2023· article· en· W4366335501 on OpenAlexaffvenue
Hiroyuki Hayashi, Ryutaro Tanizaki, Yousuke Takemura, Ran D. Goldman

Bibliographic record

VenueCanadian Family Physician · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMyofascial pain diagnosis and treatment
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMedicineSurgeryAbdominal wallNeurectomyFasciaRectus abdominis muscleAbdominal painCutaneous nervePathology

Abstract

fetched live from OpenAlex

QUESTION: I frequently see adolescents with recurrent abdominal pain in my family medicine clinic. While the diagnosis frequently is a benign condition such as constipation, I recently heard that after 2 years of recurrent pain, an adolescent was diagnosed with anterior cutaneous nerve entrapment syndrome (ACNES). How is this condition diagnosed? What is the recommended treatment? ANSWER: Anterior cutaneous nerve entrapment syndrome, first described almost 100 years ago, is caused by entrapment of the anterior branch of the abdominal cutaneous nerve as it pierces the anterior rectus abdominis muscle fascia. The limited awareness of the condition in North America results in misdiagnosis and delayed diagnosis. Carnett sign-in which pain worsens when using a "hook-shaped" finger to palpate a purposefully tense abdominal wall-helps to confirm if pain originates from the abdominal viscera or from the abdominal wall. Acetaminophen and nonsteroidal anti-inflammatory drugs were not found to be effective, but ultrasound-guided local anesthetic injections seem to be an effective and safe treatment for ACNES, resulting in relief of pain in most adolescents. For those with ACNES and ongoing pain, surgical cutaneous neurectomy by a pediatric surgeon should be considered.

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)0.000
metaresearch head score (Gemma)0.003
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.024
Threshold uncertainty score0.049

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0060.001

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 head0.007
GPT teacher head0.210
Teacher spread0.204 · 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

Citations1
Published2023
Admission routes2
Has abstractyes

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