Bibliographic record
Abstract
To the Editor; With great interest I read the article by Saginur et al, titled ‘Antitissue transglutaminase antibody determination versus upper endoscopic biopsy diagnosis of paediatric celiac disease’, regarding the use of a blood test (antitissue transglutaminase [aTTG] level) to distinguish children with celiac disease from children without to avoid performing an upper endoscopic biopsy. This is very relevant and, if adequately distinguishing, may prevent a large number of unnecessary endoscopies. The authors describe their study population, and in the results tell us that the specificity is very high, but sensitivity is low. A low sensitivity would mean that, of all patients with the disease, only few tested positive through the aTTG testing. The sensitivity of a test is its ability to recognize correctly persons who have a disease or condition. The specificity of a test is the ability of a test to recognize correctly persons who do not have a disease or condition (1). The authors state that, with increasing thresholds (≥70 U/mL, ≥100 U/mL, ≥200 U/mL), the specificity increases (97%, 97% and 100%, respectively) and the sensitivity decreases (52%, 39% and 30%, respectively). With a test that has a positive correlation with disease (a higher value of aTTG correlates with a higher chance of disease; Figure 1 in the article), this does not make sense, and I would expect the sensitivity to increase and the specificity to decrease.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.015 | 0.012 |
| Insufficient payload (model declined to judge) | 0.036 | 0.020 |
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".