Treatment Algorithm for Chronic Idiopathic Constipation and Constipation-Predominant Irritable Bowel Syndrome Derived from a Canadian National Survey and Needs Assessment on Choices of Therapeutic Agents
Bibliographic record
Abstract
Background. Chronic idiopathic constipation (CIC) and constipation-predominant irritable bowel syndrome (IBS-C) are common functional lower gastrointestinal disorders that impair patients’ quality of life. In a national survey, we aimed to evaluate (1) Canadian physician practice patterns in the utilization of therapeutic agents listed in the new ACG and AGA guidelines; (2) physicians satisfaction with these agents for their CIC and IBS-C patients; and (3) the usefulness of these new guidelines in their clinical practice. Methods. A 9-item questionnaire was sent to 350 Canadian specialists to evaluate their clinical practice for the management of CIC and IBS-C. Results. The response rate to the survey was 16% ( n=55 ). Almost all (96%) respondents followed a standard, stepwise approach for management while they believed that only 24% of referring physicians followed the same approach. Respondents found guanylyl cyclase C (GCC) agonist most satisfying when treating their patients. Among the 69% of respondents who were aware of published guidelines, only 50% found them helpful in prioritizing treatment choices and 69% of respondents indicated that a treatment algorithm, applicable to Canadian practice, would be valuable. Conclusion. Based on this needs assessment, a treatment algorithm was developed to provide clinical guidance in the management of IBS-C and CIC in Canada.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".