Development and validation of a constipation treatment toolkit for patients on hemodialysis
Bibliographic record
Abstract
INTRODUCTION: The cause of constipation is multifactorial and common problem for patients on hemodialysis. A lack of strong evidence on suitable treatment strategies means there is an unorganized approach to selecting therapies, which can exacerbate constipation or worsen symptoms. Clinicians and patients would benefit from a content and face validated treatment algorithm for treating constipation. In this study, our objective was to develop and content and face validate a constipation treatment toolkit for patients on hemodialysis, consisting of treatment algorithm, and patient information tools (pamphlet and video). METHODS: Literature searches were performed to develop an initial toolkit using Lynn's method for developing content-valid clinical tools. Content and face validity were evaluated as per Lynn's method for determining content validity; the algorithm was evaluated by Canadian nephrology clinicians, while patient information tools were evaluated by clinicians and patients. Components were rated on a Likert scale for content relevance and on a 5-point scale for face validity. After each round, the content validity index (CVI) score was calculated and revisions were made based on feedback. FINDINGS: A total of 23 clinicians and 15 patients were interviewed across three validation rounds. After three rounds, the treatment algorithm achieved content (overall CVI = 0.93) and face (91% agreement) validity. Our patient information tools achieved content and face validity (pamphlet overall CVI = 0.99, 85.5% agreement; video overall CVI = 0.99, 90.5% agreement). DISCUSSION: A treatment algorithm and patient information toolkit for the treatment of constipation in patients on hemodialysis were content and face validated via expert review. Further research will be needed to ascertain the effectiveness and implementation of this toolkit.
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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 | 0.052 | 0.087 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".