Content validation of a daily patient-reported outcome measure for assessing symptoms in patients with Small Intestinal Bacterial Overgrowth
Bibliographic record
Abstract
PURPOSE: The aim of this study was to generate evidence supporting the development and content validity of a new PRO instrument, the Small Intestinal Bacterial Overgrowth (SIBO) Symptom Measure (SSM) daily diary. The SSM assesses symptom severity in SIBO patients, with the ultimate goal of providing a fit for purpose PRO for endpoint measurement. METHODS: Qualitative research included 35 SIBO patients in three study stages, using a hybrid concept elicitation (CE)/cognitive interview (CI) method with US patients, ≥ 18 years. Stage 1 included a literature review, clinician interviews, and initial CE interviews with SIBO patients to identify symptoms important to patients for inclusion in the SSM. Stage 2 included hybrid CE/CI to learn more about patients' SIBO experience and test the draft SSM. Finally, stage 3 used CIs to refine the instrument and test its content validity. RESULTS: In stage 1 (n = 8), 15 relevant concepts were identified, with items drafted based on the literature review/clinician interviews and elicitation work. Within stage 2 (n = 15), the SSM was refined to include 11 items; with wording revised for three items. Stage 3 (n = 12) confirmed the comprehensiveness of the SSM, as well as appropriateness of the item wording, recall period, and response scale. The resulting 11-item SSM assesses the severity of bloating, abdominal distention, abdominal discomfort, abdominal pain, flatulence, physical tiredness, nausea, diarrhea, constipation, appetite loss, and belching. CONCLUSIONS: This study provides evidence supporting the content validity of the new PRO. Comprehensive patient input ensures that the SSM is a well-defined measure of SIBO, ready for psychometric validation studies.
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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 | 0.059 | 0.155 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".