Standardized system and App for continuous patient symptom logging in gastroduodenal disorders: Design, implementation, and validation
Bibliographic record
Abstract
Abstract Background Functional gastroduodenal disorders include functional dyspepsia, chronic nausea and vomiting syndromes, and gastroparesis. These disorders are common, but their overlapping symptomatology poses challenges to diagnosis, research, and therapy. This study aimed to introduce and validate a standardized patient symptom‐logging system and App to aid in the accurate reporting of gastroduodenal symptoms for clinical and research applications. Methods The system was implemented in an iOS App including pictographic symptom illustrations, and two validation studies were conducted. To assess convergent and concurrent validity, a diverse cohort with chronic gastroduodenal symptoms undertook App‐based symptom logging for 4 h after a test meal. Individual and total post‐prandial symptom scores were averaged and correlated against two previously validated instruments: PAGI‐SYM (for convergent validity) and PAGI‐QOL (for concurrent validity). To assess face and content validity, semi‐structured qualitative interviews were conducted with patients. Key Results App‐based symptom reporting demonstrated robust convergent validity with PAGI‐SYM measures of nausea ( r S =0.68), early satiation ( r S =0.55), bloating ( r S =0.48), heartburn ( r S =0.47), upper gut pain ( r S =0.40), and excessive fullness ( r S =0.40); all p < 0.001 ( n = 79). The total App‐reported Gastric Symptom Burden Score correlated positively with PAGI‐SYM ( r S =0.56; convergent validity; p < 0.001), and negatively with PAGI‐QOL ( r S = −0.34; concurrent validity; p = 0.002). Interviews demonstrated that the pictograms had adequate face and content validity. Conclusions and Inferences The continuous patient symptom‐logging App demonstrated robust convergent, concurrent, face, and content validity when used within a 4‐h post‐prandial test protocol. The App will enable standardized symptom reporting and is anticipated to provide utility in both research and clinical practice.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.029 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".