Review article: self‐report measures to evaluate constipation
Bibliographic record
Abstract
BACKGROUND: Constipation is a subjective phenomenon, and as such must be evaluated using patient self-report. Valid and reliable measures of constipation are essential to standardize the diagnosis, assess the severity and evaluate the effectiveness of treatments. AIM: To compare and contrast published self-report measures of constipation in terms of development, content, general characteristics, psychometric properties and clinical utility. METHODS: MEDLINE (1966-2007), CINAHL (1980-2007), Cochrane (1993-2007) and Web of Science (1995-2007) were searched to identify self-report measures of constipation. Measures of constipation were selected if they: (i) were self-report measures that measured only constipation; (ii) had undergone psychometric testing; (iii) were used in adults and (iv) were written in English. RESULTS: Seven self-report measures of constipation were identified. The content areas evaluated by these measures varied. Only two measures had adequate validity and reliability, sensitivity to change, or were tested in more than one sample. CONCLUSIONS: Findings from this review suggest that the Chinese Constipation Questionnaire and the Patient Assessment of Constipation-Symptom Questionnaire demonstrate adequate psychometric properties for a constipation measure. Additional research is warranted to refine or develop a more comprehensive self-report measure to evaluate constipation in adults.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".