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Record W4378194195 · doi:10.1101/2023.05.18.23290134

Gastric Alimetry <sup>®</sup> improves patient phenotyping in gastroduodenal disorders compared to gastric emptying scintigraphy alone

2023· preprint· en· W4378194195 on OpenAlexaff
William Wang, Daphne Foong, Stefan Calder, Gabriel Schamberg, Chris Varghese, Jan Tack, William Xu, Charlotte Daker, D A Carson, Stephen Waite, Thomas Hayes, Peng Du, Thomas L. Abell, Henry P. Parkman, I‐Hsuan Huang, Vivian Rodrigues Fernandes, Christopher N. Andrews, Armen A. Gharibans, Vincent Ho, Greg O’Grady

Bibliographic record

VenuemedRxiv · 2023
Typepreprint
Languageen
FieldMedicine
TopicGastrointestinal motility and disorders
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsGastric emptyingScintigraphyGastroenterologyMedicineInternal medicineTechnetiumRadionuclide imagingNuclear medicineStomach

Abstract

fetched live from OpenAlex

Abstract Objectives Gastric emptying testing (GET) assesses gastric motility, however is non-specific and insensitive for neuromuscular disorders. Gastric Alimetry® (GA) is a new medical device combining non-invasive gastric electrophysiological mapping and validated symptom profiling. This study assessed patient-specific phenotyping using GA compared to GET. Methods Patients with chronic gastroduodenal symptoms underwent simultaneous GET and GA, comprising a 30-minute baseline, 99m TC-labelled egg meal, and 4-hour postprandial recording. Results were referenced to normative ranges. Symptoms were profiled in the validated GA App and phenotyped using rule-based criteria based on their relationships to the meal and gastric activity: i) sensorimotor; ii) continuous; and iii) other. Results 75 patients were assessed; 77% female. Motility abnormality detection rates were: GET 22.7% (14 delayed, 3 rapid); GA spectral analysis 33.3% (14 low rhythm stability / low amplitude; 5 high amplitude; 6 abnormal frequency); combined yield 42.7%. In patients with normal spectral analysis, GA symptom phenotypes included: sensorimotor 17% (where symptoms strongly paired with gastric amplitude; median r=0.61); continuous 30%; other 53%. GA phenotypes showed superior correlations with GCSI, PAGI-SYM, and anxiety scales, whereas Rome IV Criteria did not correlate with psychometric scores (p&gt;0.05). Delayed emptying was not predictive of specific GA phenotypes. Conclusions GA improves patient phenotyping in chronic gastroduodenal disorders in the presence and absence of motility abnormalities with improved correlation with symptoms and psychometrics compared to gastric emptying status and Rome IV criteria. These findings have implications for the diagnostic profiling and personalized management of gastroduodenal disorders. Study Highlights 1) WHAT IS KNOWN Chronic gastroduodenal symptoms are common, costly and greatly impact on quality of life There is a poor correlation between gastric emptying testing (GET) and symptoms Gastric Alimetry® is a new medical device combining non-invasive gastric electrophysiological mapping and validated symptom profiling 2) WHAT IS NEW HERE Gastric Alimetry generates a 1.5x higher yield for motility abnormalities than GET With symptom profiling, Gastric Alimetry identified 2.7x more specific patient categories than GET Gastric Alimetry improves clinical phenotyping, with improved correlation with symptoms and psychometrics compared to GET

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.023
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0030.003
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.002
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 head0.027
GPT teacher head0.268
Teacher spread0.241 · 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.

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
Published2023
Admission routes1
Has abstractyes

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