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Record W3203942017 · doi:10.14740/ijcp422

Challenges in Differentiating Between Solitary Rectal Ulcer Syndrome and Inflammatory Bowel Disease in the Pediatric Population

2021· article· en· W3203942017 on OpenAlexvenueno aff
Shouli Tung, Paige Richards, Gabriele Hunter, John Tung

Bibliographic record

VenueInternational Journal of Clinical Pediatrics · 2021
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicInflammatory Bowel Disease
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineInflammatory bowel diseaseConstipationDiseaseDifferential diagnosisMedical diagnosisMalignancyAbdominal painDiarrheaGastroenterologyPopulationUlcerative colitisColitisInternal medicineRadiologyPathology

Abstract

fetched live from OpenAlex

Solitary rectal ulcer syndrome (SRUS) is a benign rectal disease that is rare in pediatric populations due to its underdiagnosis and misdiagnosis in children. It is often misdiagnosed as malignancy, Crohn’s disease, and ulcerative colitis due to its wide and varying clinical presentations. Both SRUS and inflammatory bowel disease (IBD) can present with rectal bleeding, constipation, diarrhea, and abdominal pain. Furthermore, macroscopic ulcers and inflammation can be seen in both diseases, making it difficult to diagnose without a biopsy. We present two cases in the pediatric population whose diagnoses of SRUS were delayed because the symptoms and macroscopic findings initially supported the differential diagnosis of IBD. These cases emphasize the difficulty and importance of differentiating between IBD and SRUS, and should encourage practitioners to include this differential diagnosis earlier on to improve diagnostic accuracy and begin implementing effective treatment. This can eventually decrease overall treatment time, unnecessary surgeries and diagnostic testing, and increase the emotional reassurance of the benign nature of the disease. Int J Clin Pediatr. 2021;10(2-3):57-63 doi: https://doi.org/10.14740/ijcp422

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)0.002
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 score0.005
Threshold uncertainty score0.413

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
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 head0.041
GPT teacher head0.344
Teacher spread0.303 · 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

Citations3
Published2021
Admission routes1
Has abstractyes

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