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Gene Expression Profiling in Pediatric Appendicitis

2024· letter· en· W4391933016 on OpenAlexafffundabout
Bhavjinder K. Dhillon, Simone Kortbeek, Arjun Baghela, Mary Brindle, Dori-Ann Martin, Craig N. Jenne, Hans J. Vogel, Amy Huei‐Yi Lee, Graham C. Thompson, Robert E. W. Hancock

Bibliographic record

VenueJAMA Pediatrics · 2024
Typeletter
Languageen
FieldMedicine
TopicAppendicitis Diagnosis and Management
Canadian institutionsAlberta Children's HospitalSimon Fraser UniversityUniversity of CalgaryUniversity of British Columbia
FundersCanadian Institutes of Health Research
KeywordsMedicineAppendicitisAbdominal painPopulationEmergency departmentInternal medicineIntensive care medicineSurgery

Abstract

fetched live from OpenAlex

Importance: Appendicitis is the most common indication for urgent surgery in the pediatric population, presenting across a range of severity and with variable complications. Differentiating simple appendicitis (SA) and perforated appendicitis (PA) on presentation may help direct further diagnostic workup and appropriate therapy selection, including antibiotic choice and timing of surgery. Objective: To provide a mechanistic understanding of the differences in disease severity of appendicitis with the objective of developing improved diagnostics and treatments, specifically for the pediatric population. Design, Setting, and Participants: The Gene Expression Profiling of Pediatric Appendicitis (GEPPA) study was a single-center prospective exploratory diagnostic study with transcriptomic profiling of peripheral blood collected from a cohort of children aged 5 to 17 years with abdominal pain and suspected appendicitis between November 2016 and April 2017 at the Alberta Children's Hospital in Calgary, Alberta, Canada, with data analysis reported in August 2023. There was no patient follow-up in this study. Exposure: SA, PA, or nonappendicitis abdominal pain. Main Outcomes and Measures: Blood transcriptomics was used to develop a hypothesis of underlying mechanistic differences between SA and PA to build mechanistic hypotheses and blood-based diagnostics. Results: Seventy-one children (mean [SD] age, 11.8 [3.0] years; 48 [67.6%] male) presenting to the emergency department with abdominal pain and suspected appendicitis were investigated using whole-blood transcriptomics. A central role for immune system pathways was revealed in PA, including a dampening of major innate interferon responses. Gene expression changes in patients with PA were consistent with downregulation of immune response and inflammation pathways and shared similarities with gene expression signatures derived from patients with sepsis, including the most severe sepsis endotypes. Despite the challenges in identifying early biomarkers of severe appendicitis, a 4-gene signature that was predictive of PA compared to SA, with an accuracy of 85.7% (95% CI, 72.8-94.1) was identified. Conclusions: This study found that PA was complicated by a dysregulated immune response. This finding should inform improved diagnostics of severity, early management strategies, and prevention of further postsurgical complications.

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)0.001
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 score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.020
GPT teacher head0.270
Teacher spread0.250 · 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

Citations9
Published2024
Admission routes3
Has abstractyes

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