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Record W2329178925 · doi:10.1097/sga.0000000000000210

The Gastrointestinal Pain Pointer

2015· article· en· W2329178925 on OpenAlexaboutno aff
Wendy A. Henderson, Bridgett Rahim-Williams, Kevin H. Kim, LeeAnne B. Sherwin, Sarah K. Abey, Angela C. Martino, Nicolaas H. Fourie, Margaret Heitkemper, Anthony Zuccolotto

Bibliographic record

VenueGastroenterology Nursing · 2015
Typearticle
Languageen
FieldMedicine
TopicGastrointestinal motility and disorders
Canadian institutionsnot available
FundersNational Institute on Minority Health and Health DisparitiesNational Institute of Nursing ResearchNational Institutes of HealthNational Center for Theoretical Sciences
KeywordsMedicineIrritable bowel syndromeAbdominal painMcGill Pain QuestionnairePhysical therapyPointer (user interface)AbdomenProspective cohort studyInternal medicineSurgeryVisual analogue scale

Abstract

fetched live from OpenAlex

Abdominal pain is a chronic condition experienced by approximately 20% of individuals in the United States. The purpose of the study was to assess the validity of the Gastrointestinal Pain Pointer as a measure of abdominal pain intensity. A prospective longitudinal time-series study design was utilized. The sample included 93 outpatients (58.1% female). Participants met Rome III criteria for irritable bowel syndrome (n = 32) or were healthy controls (n = 61). The Gastrointestinal Pain Pointer, a new electronic pain assessment tool, was used to assess self-reported abdominal pain intensity among participants before and after ingestion of an intestinal permeability test solution across 11 time points over a 5-hour time period. The results were compared with the Short-Form McGill Pain Questionnaire. The Gastrointestinal Pain Pointer was found to be valid in the assessment of abdominal pain intensity. The tool is a novel and valid measure of abdominal pain intensity that enhances the ability for clinicians to better quantify, in real time, patient-related pain outcomes for both clinical care and research.

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 score0.087
Threshold uncertainty score0.537

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 studies0.0000.001
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 head0.029
GPT teacher head0.283
Teacher spread0.254 · 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

Citations11
Published2015
Admission routes1
Has abstractyes

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