Real-time Symptom Assessment in Patients With Endometriosis: Psychometric Evaluation of an Electronic Patient-Reported Outcome Measure, Based on the Experience Sampling Method
Bibliographic record
Abstract
BACKGROUND: The experience sampling method (ESM) holds advantages over traditional retrospective questionnaires including a high ecological validity, no recall bias, the ability to assess fluctuation of symptoms, and the ability to analyze the temporal relationship between variables. OBJECTIVE: This study aimed to evaluate the psychometric properties of an endometriosis-specific ESM tool. METHODS: This is a short-term follow-up prospective study, including patients with premenopausal endometriosis aged ≥18 years who reported dysmenorrhea, chronic pelvic pain, or dyspareunia between December 2019 and November 2020. An ESM-based questionnaire was sent out by a smartphone application 10 times a day during 1 week on randomly chosen moments. Additionally, patients completed questionnaires concerning demographics, end-of-day pain scores, and end-of-week symptom scores. The psychometric evaluation included compliance, concurrent validity, and internal consistency. RESULTS: Twenty-eight patients with endometriosis completed the study. Compliance for answering the ESM questions was as high as 52%. End-of-week pain scores were higher than ESM mean scores and showed peak reporting. ESM scores showed strong concurrent validity when compared with symptoms scored by the Gastrointestinal Symptom Rating Scale-Irritable Bowel Syndrome, 7-item Generalized Anxiety Disorders Scale, 9-question Patient Health Questionnaire, and the majority of questions of the 30-item Endometriosis Health Profile. Cronbach α coefficients demonstrated a good internal consistency for abdominal symptoms, general somatic symptoms, and positive affect, and an excellent internal consistency for negative affect. CONCLUSIONS: This study supports the validity and reliability of a newly developed electronic instrument for the measurement of symptoms in women with endometriosis, based on momentary assessments. This ESM patient-reported outcome measure has the advantage of providing a more detailed view on individual symptom patterns and offers the possibility for patients to have insight in their symptomatology, leading to more individualized treatment strategies that can improve the quality of life of women with endometriosis.
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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 | 0.016 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".