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Record W2030857310 · doi:10.1055/s-0030-1262842

Does Pre-Biopsy Contrast Enema Delay the Diagnosis of Long Segment Hirschsprung's Disease?

2010· article· en· W2030857310 on OpenAlexaff
J. Z. Chen, D. H. Jamieson, Erik D. Skarsgard

Bibliographic record

VenueEuropean Journal of Pediatric Surgery · 2010
Typearticle
Languageen
FieldMedicine
TopicCongenital gastrointestinal and neural anomalies
Canadian institutionsUniversity of British ColumbiaBC Children's Hospital
Fundersnot available
KeywordsMedicineHirschsprung's diseaseEnemaBiopsyDiseaseRadiologySurgeryInternal medicine

Abstract

fetched live from OpenAlex

INTRODUCTION: The diagnosis of long segment Hirschsprung's disease (LSHD) is frequently delayed. Our purpose was to: 1) summarize contrast enema (CE) findings in patients with LSHD, and 2) evaluate the utility of CE by comparing LSHD patients managed with/without pre-biopsy CE. METHODS: All LSHD cases (transition zone [TZ] proximal to the splenic flexure) treated between 1984 and 2009 were stratified according to whether a pre-biopsy CE was done (Group 1) or not (Group 2). CE were reviewed by a single pediatric radiologist, and the original reports were categorized as "helpful", "inconclusive" or "misleading". Group comparisons included elapsed days from admission to diagnostic rectal biopsy/first operation and initial hospitalization length of stay (LOS). RESULTS: 29 patients (16 in Group 1; 13 in Group 2) were identified. CE review revealed TZ in 7/16 (44%); and of these, 6 (86%) underestimated the actual aganglionic segment length. 6/16 (38%) original CE reports were "misleading". Overall, Group 1 patients experienced a significant delay in time to biopsy (p=0.047), first operation (p=0.005), and showed a trend towards prolonged LOS. CONCLUSIONS: Pre-biopsy CE offers little to the diagnosis of LSHD and may contribute to diagnosis/treatment delays. Even if a TZ is recognized in biopsy proven HD, the predicted aganglionic segment length should not guide the operative planning.

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)0.001
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 score0.020
Threshold uncertainty score0.363

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.016
GPT teacher head0.229
Teacher spread0.213 · 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

Citations9
Published2010
Admission routes1
Has abstractyes

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