The authors respond;
Bibliographic record
Abstract
We thank Dr Jong for his comments regarding our article.We are happy to clarify the concerns expressed regarding sensitivity and specificity, particularly because this terminology is often confusing, even more so when we are using the same test for diagnosis versus screening. In this regard, we find the mnemonics ‘Sp-in’, relating to specificity for ruling in the diagnosis, and ‘Sn-out’, relating to sensitivity in screening for subsequent diagnostic testing, to be useful. In applying an aTTG cutoff of ≥200 U/mL, we are aiming for a high specificity – to rule in the disease (Sp-in). Above this cut-off, celiac disease was present in 100% of the children studied. However, the sensitivity is low because we know that some patients with values below this cut-off also have celiac disease (because they screened positive and had a biopsy performed). Using aTTG ≥70 U/mL to screen for celiac disease is a highly sensitive test and enables us to rule out the disease in patients who test negative.
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 | 0.005 | 0.083 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.012 | 0.014 |
| Insufficient payload (model declined to judge) | 0.106 | 0.065 |
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".