Bibliographic record
Abstract
The prevalence reported in the study is 0.9% (1). The methods and interpretation of the results prompt a certain amount of criticism, however. The title of the article includes the term “prevalence of celiac disease”. What was studied in actual fact, however, was the frequency of positive transglutaminase antibodies (tTG-AB), and the prevalence of celiac disease was estimated from this. Celiac disease is an immunologically mediated enteropathy. For a diagnosis without histological confirmation, the ESPGHAN guideline (2012) has recommended a tTG-AB titer above the 10-fold cut-off, and additionally the confirmation of positive endomysium antibodies (EMA-AB). The study determined only the tTG-AB, although JH, co-initiator of the study, the Robert Koch-Institute, which conducted the analysis, pointed out the necessity of also measuring EMA-AB several times. When both antibodies were measured, the sensitivity and specificity in mass screenings were substantially improved (2, 3). When the frequency of the tTG-AB values above the 10-fold cut-off is considered in the present study, the prevalence of celiac disease according to Table 1 is 0.4% (95% confidence interval [CI]: 0.3%; 0.5%), which seems to reflect reality. Such an assessment, while including positive EMA-AB, is also consistent to two cohort studies in German adults, of 0.2% and 0.3% from 2010 (4). A recent Canadian study found biopsy-proofed celiac disease in 13.3% of children with up to threefold-cut-off positive tTG-AB but negative EMA-AB (2). As the maximum specificity of tTG-AB is 95%, 5% of false results are to be expected. The conclusion by the authors of the study (1) that the prevalence of celiac disease of 0.9% is comparable with that in other European countries, is thus not correct, as enormous differences exist in this respect between European countries and further abroad.
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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 | 0.006 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.028 | 0.016 |
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".