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Record W4388730279 · doi:10.57264/cer-2023-0054

Gastrointestinal manifestations in pediatric and adult patients with Rett syndrome: an analysis of US claims and physician survey data

2023· article· en· W4388730279 on OpenAlexaff
Damian M. May, Jeffrey L. Neul, J. Eric Piña-Garza, Kalé Kponee-Shovein, Ambika Satija, Malena Mahendran, Nathaniel Downes, Kristy Sheng, Neema Lema, Andra Boca, Patrick Lefèbvre, Victor Abler, James M. Youakim, Wendy Y. Cheng

Bibliographic record

VenueJournal of Comparative Effectiveness Research · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetics and Neurodevelopmental Disorders
Canadian institutionsGroup for Research in Decision Analysis
FundersEunice Kennedy Shriver National Institute of Child Health and Human Development
KeywordsMedicineRett syndromeMedical costsPediatricsHealth insuranceMedical careYoung adultHealth careInternal medicineFamily medicine

Abstract

fetched live from OpenAlex

Aim: Patients with Rett syndrome (RTT) experience gastrointestinal (GI) manifestations. This study aimed to describe the prevalence of GI manifestations and the associated medical costs in patients with RTT in the USA. Patients & Methods: The study combined an insurance claims database analysis with a survey of 100 physicians experienced in RTT management. Results: GI manifestations affected 43.0% of 5940 patients, with increased prevalence in pediatric patients (45.6%) relative to adult patients (40.2%). Annualized mean medical cost of managing GI manifestations was $4473. Only 5.9–8.2% of neurologists and pediatricians ranked GI symptom management among the five most important treatment goals. Conclusion: Patients with RTT experience a high burden of GI manifestations, which translate to considerable medical costs. Importantly, the prevalence of GI manifestations was likely underestimated in this study, as only those symptoms which resulted in a healthcare encounter were captured.

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)0.000
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 score0.005
Threshold uncertainty score0.307

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0000.000

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 head0.069
GPT teacher head0.386
Teacher spread0.317 · 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

Citations6
Published2023
Admission routes1
Has abstractyes

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