Comment on: real-life effectiveness of canakinumab in cryopyrin-associated periodic syndrome: reply
Bibliographic record
Abstract
Sir, We thank Guido Junge and Uwe Machein for their comments on our recent letter [1, 2]. They make the point that the expression standard dose, which we have used in our article, might be misleading and the expression starting dose might be more accurate. They cite the canakinumab summary of product characteristics [3] in which different starting doses are mentioned and increasing the dose is recommended in case full treatment response is not achieved with the starting dose [2]. We do appreciate this clarification. The proceedings outlined in the summary of product characteristics support the treat-to-target strategy, which we plead for in our article. However, when gathering the data for this study, we were surprised to learn that a considerable number of patients had been maintained on the starting dose, without achieving complete remission. Therefore, the intention of our article is to alert physicians with the care of cryopyrin-associated periodic syndrome patients that adherence to the starting dose in spite of ongoing inflammation might pose their patients at considerable risk for severe long-term disease sequelae. Funding: No specific funding was received from any bodies in the public, commercial or not-for-profit sectors to carry out the work described in this manuscript. Disclosure statement: J.K.-D. performed clinical studies with and received honoraria from Novartis. The other author has declared no conflicts of interest.
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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 | 0.003 | 0.028 |
| 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 | 0.003 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.056 | 0.038 |
| Insufficient payload (model declined to judge) | 0.007 | 0.007 |
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".