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Record W4410122211 · doi:10.24908/pocusj.v10i01.17744

Identification of Pediatric Retrocecal Appendicitis Using Point of Care Ultrasound (POCUS)

2025· article· en· W4410122211 on OpenAlexvenueno aff
Carl Kaplan, Raizada Vaid, Michael Secko

Bibliographic record

VenuePOCUS Journal · 2025
Typearticle
Languageen
FieldMedicine
TopicAppendicitis Diagnosis and Management
Canadian institutionsnot available
Fundersnot available
KeywordsAppendicitisMedicineRadiologyUltrasoundAppendixPerforationAbdomenMagnetic resonance imagingPoint of care ultrasoundGeneral surgery

Abstract

fetched live from OpenAlex

Acute appendicitis is the most common pediatric surgical emergency. Diagnosis may be made by targeted point of care ultrasound (POCUS) of the right lower quadrant (RLQ) abdomen. This can be performed by trained emergency physicians and has similar accuracy to ultrasound performed by radiology technologists and interpreted by radiologists (RADUS) [1,2]. Pediatric patients with appendicitis may present without classical clinical signs and symptoms. Retrocecal appendicitis is often diagnosed late at perforation due to the anatomical position limiting diagnosis with ultrasound, despite the high prevalence of retrocecal appendix as an anatomic variation (up to 65%). Given the limited sensitivity for ultrasound in the diagnosis of appendicitis in patients with retrocecal appendix, these patients often undergo advanced imaging with computed tomography (CT) or magnetic resonance imaging (MRI), especially when increased abdominal wall thickness and/or high Body Mass Index (BMI) further limit the ultrasound examination [4-6]. We present a case series of retrocecal appendicitis imaged and diagnosed with POCUS, using novel transducer and patient positioning. In addition to standard graded compression of the RLQ with POCUS, this technique may add to the diagnostic accuracy of patients presenting atypically with anatomic variants.

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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.013
GPT teacher head0.304
Teacher spread0.292 · 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
Published2025
Admission routes1
Has abstractyes

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