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Record W4221022834 · doi:10.1097/pg9.0000000000000181

Evaluation of Hepatitis in Pediatric Patients With Presumed Nonalcoholic Fatty Liver Disease

2022· article· en· W4221022834 on OpenAlexafffund
Fat’hiya Al-Harthy, Neha Kamath, Lee Hill, Jelena Popov, Allison Bossert, Herbert Brill, Nikhil Pai

Bibliographic record

VenueJPGN Reports · 2022
Typearticle
Languageen
FieldMedicine
TopicLiver Disease Diagnosis and Treatment
Canadian institutionsHamilton Health SciencesMcMaster Children's HospitalWestern UniversityMcMaster University
FundersMcMaster University
KeywordsHepatologyNonalcoholic fatty liver diseaseMedicineInternal medicineGastroenterologyEtiologyLiver diseasePediatric gastroenterologyDifferential diagnosisSteatosisDiseaseChronic liver diseaseFatty liverCirrhosisPathology

Abstract

fetched live from OpenAlex

In 2017, the North American Society of Pediatric Gastroenterology, Hepatology and Nutrition published clinical practice guidelines for the assessment and diagnosis of nonalcoholic fatty liver disease (NAFLD). We determined how frequently these investigations suggest an alternate etiology for chronic hepatitis in 8- to 17-year-old patients with body mass index >85%, elevated alanine aminotransferase and radiographic steatosis, and rates of adherence to 2017 guidelines. Methods: We conducted a retrospective chart review of patients presenting to McMaster Children's Hospital from 2017-2020 for evaluation of suspected NAFLD. Bloodwork was reviewed. Results: Ninety-five patients met inclusion criteria. Abnormal bloodwork that required further testing was found in 28.4%; a different chronic liver disease was ultimately diagnosed in 11.6%. Only 9.5% received comprehensive, additional bloodwork for other causes of liver disease. Conclusion: A high proportion of patients evaluated for suspected NAFLD had bloodwork possibly suggesting an alternate diagnosis. Comprehensive testing was infrequently performed. These results reinforce the importance of maintaining a differential diagnosis among children presumed to have NAFLD.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.003
Threshold uncertainty score0.837

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0010.000

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 head0.018
GPT teacher head0.261
Teacher spread0.242 · 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
Published2022
Admission routes2
Has abstractyes

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