Longitudinal Outcomes of GastroIntestinal symptoms in Canada (LOGIC): key factors for an effective patient retention in observational studies.
Bibliographic record
Abstract
BACKGROUND: Longitudinal Outcomes of GastroIntestinal symptoms in Canada (LOGIC) is an ongoing study on irritable bowel syndrome (IBS) treatment patterns and health outcomes in routine Canadian clinical practice. Advancements in understanding IBS, a chronic multifaceted GI disorder, may be possible through methodical observational studies. The objective of this paper is to describe site recruitment techniques and extensive subject follow-up methodology used to facilitate a high return rate of questionnaires from this population-based study of subjects with IBS. METHODS: Invitation letters along with protocol synopses and preliminary site assessment questionnaires were faxed to potential sites across Canada. There were 1,556 subjects enrolled in this study from general practitioner sites (GP) and specialist sites (SP) in Canada. Subjects were compensated for the return of questionnaires reporting symptoms, quality of life, productivity, healthcare and resource utilization at baseline, Month 1, 3, 6, 9, and 12. Upon the return of questionnaires, subjects received thank you cards which included a reminder of the next questionnaire's due date. If subject questionnaires were not received within 2 weeks after the due date, the subjects received a reminder letter in the mail. RESULTS: The methodology in the LOGIC study allowed for a high patient questionnaire return rate (89%) through extensive subject reminders and follow-up. Subject participation throughout the study was not found to be linked to study site size or type (GP or SP). CONCLUSION: Questionnaire based observational studies may benefit from focusing resources on increasing questionnaire return rates to effectively maintain data reliability and also reduce non-response bias.
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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 | 0.356 | 0.572 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".