Response to Villanacci et. al.
Bibliographic record
Abstract
We would like to thank Dr Villanacci and colleagues for their letter, and are pleased that they agree with our conclusion that mucosal healing by both endoscopic and histological healing should be a target for therapy in ulcerative colitis.1 We are of course aware of their paper in this journal, concluding that the combination of basal plasma cells and eosinophils is a good diagnostic indicator for IBD.2 Both elements [basal plasmacytosis and mucosal eosinophilia, albeit not basal], were also found to be valid criteria for predicting relapse, in the paper by Bessissow.3 What are less clear are the criteria for defining basal plasmacytosis. This is invariably more abundant in the left colon than the right, to the point where plasma cells can be found normally in the vicinity of the ileo-caecal valve[ICV], which is a feature that probably applies more in adults than in the children, so may be age related. The corollary of this is that an absence of basal plasmacytosis as an indicator of long-term remission is probably far more meaningful in the left colon than the right, especially in adults. This raises a difficult issue, because if basal plasma cells are normal in the region of the ICV, their presence in this location should not be associated with an increased risk of relapse in ulcerative colitis [UC]. However, we see no contradiction in the presence of basal plasma cells in some patients when initially diagnosed, but not later in relapse.
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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 | 0.002 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.038 | 0.031 |
| Insufficient payload (model declined to judge) | 0.011 | 0.012 |
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".