Nationwide survey of coeliac disease serology testing in the UK
Bibliographic record
Abstract
OBJECTIVE: Recent evidence supports diagnosing coeliac disease without biopsy in patients with significantly elevated tissue transglutaminase (IgA-tTG) antibodies. However, the implementation of this no-biopsy approach relies on accurate and consistent serological testing across laboratories. In this nationwide survey, we aimed to evaluate the availability and variability of coeliac disease testing across the UK. METHODS: We conducted a cross-sectional telephone survey of biomedical scientists and laboratory managers from National Health Service trusts and health boards across England, Wales, Scotland, and Northern Ireland. Data collected included assay types, reporting methods, upper limit of normal (ULN) thresholds, turnaround times, total IgA testing, and anti-endomysial antibodies (EMAs) availability. RESULTS: A total of 356 sites were approached, with a 96% response rate (n=342). Of responding sites, 177 performed coeliac serology tests in-house, while 165 transferred samples externally. Among sites performing tests, 12 different IgA-tTG assays were identified, with considerable variability in ULN thresholds ranging from 3 to 30 IU/mL, even within laboratories using the same assays. The median turnaround time for IgA-tTG results was 7 days (range 1-21 days). Only 43% of laboratories routinely measured total IgA when IgA-tTG was requested. EMA testing was available in 83% of laboratories. CONCLUSION: Significant variability exists in coeliac serology testing across UK laboratories which poses a challenge for the implementation of the no-biopsy approach in clinical practice. Efforts to standardise serological testing are urgently needed. Until such standardisation is achieved, local assay validation remains critical.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".