Fluctuation of gastrointestinal symptoms in the community: a 10‐year longitudinal follow‐up study
Bibliographic record
Abstract
BACKGROUND: There are few studies examining the stability of gastrointestinal symptoms during prolonged periods of follow-up. AIM: To examine this issue in individuals previously recruited into a community screening programme for Helicobacter pylori providing symptom data at study entry. METHODS: All traceable participants were sent dyspepsia and IBS questionnaires by post at 10 years. Symptom subgroups were assigned at baseline and 10-year follow-up. Individuals symptomatic at both time points who changed subgroup were compared with those symptomatic and remaining in the same subgroup. RESULTS: Three-thousand eight hundred and nineteen individuals provided data. At baseline, 2417 (63%) were asymptomatic or did not meet diagnostic criteria for a subgroup. Of these, 1648 (68%) remained asymptomatic at 10 years, whilst 769 (32%) reported symptoms. Of the 1402 individuals symptomatic at baseline, 404 (29%) remained in the same subgroup at 10 years, 603 (43%) changed subgroup and symptoms resolved or did not meet criteria for a subgroup in 395 (28%). Symptom stability was more likely in males [odds ratio (OR): 1.50; 99% confidence interval (CI): 0.97-2.31] and older subjects (OR per year: 1.09; 99% CI: 1.01-1.17). CONCLUSION: Of those subjects symptomatic at baseline, almost three-quarters remained symptomatic at 10 years, but over 40% changed symptom subgroup.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".