Cost-effectiveness of faecal calprotectin used in primary care in the diagnosis of inflammatory bowel disease
Bibliographic record
Abstract
OBJECTIVE: Inflammatory bowel disease (IBD) is a chronic, autoimmune, gastrointestinal disorder. Canada has one of the highest prevalence and incidence rates of IBD in the world. Diagnosis is challenging due to the similarity of symptoms to functional gastrointestinal disorders. Faecalcalprotectin (FC) is a biomarker for active mucosal inflammation and has proven effective in the diagnosis of IBD. Our study objective was to assess the cost-effectiveness of adding an FC test compared with standard practice (blood test) in primary care among adult patients presenting with gastrointestinal symptoms. DESIGN: We constructed a decision analytic tree with a 1-year time horizon. The cut-off level of 100 µg/g was used for FC testing. Probabilistic analyses were conducted for the base case and all scenarios. SETTING: Canadian health sector perspective. POPULATION: A hypothetical cohort of adult patients presenting with gastrointestinal symptoms in the primary care setting. INTERVENTIONS: FC test compared with blood test. MAIN OUTCOME MEASURES: Costs, quality-adjusted life years (QALYs), incremental cost-effectiveness ratio (ICER) of FC test expressed as cost per QALY gained compared with blood test and time to IBD diagnosis. RESULTS: FC testing is expected to cost more ($C295.1 vs $C273.9) than standard practice but yield little higher QALY (0.751vs0.750). The ICER of FC test was $C20 323 per QALY. Probabilistic analysis demonstrated that at a willingness-to-pay threshold of $C50 000 per QALY, there was 81.3% probability of FC test being cost-effective. The use of FC test in primary care reduced the time to IBD diagnosis by 40.0 days (95% CI 16.3 to 65.3 days), compared with blood testing alone. CONCLUSIONS: Based on this analysis of short-term outcomes, screening adult patients in primary care using FC test at a cut-off level of 100 µg/g is expected to be cost-effective 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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".