Comment on: Infliximab, etanercept and adalimumab for the treatment of ankylosing spondylitis: cost-effectiveness evidence and NICE guidance: reply
Bibliographic record
Abstract
Sir, In our editorial [1], we outlined the considerations National Institute for Health and Clinical Excellence (NICE) has had to make when considering extremely different estimates of cost effectiveness made by sponsors of the three TNF antagonists and an independent assessment group. Kobelt, an author of one of the sponsored studies questions the factual basis of the editorial and integrity of the authors [2]. First, Kobelt [2] correctly states that the £19 196 per quality adjusted life year (QALY) figure we quote does not appear in her publication but this has no effect on the issues raised in the editorial. The £19 196 per QALY was the original figure submitted to NICE by Schering Plough (SP) [3] (for the ASSERT trial) and Kobelt accepts that this was based on a model that contained a significant error, uncovered by the NICE process. A revised figure of £26 751 was resubmitted to NICE [4] (for the ASSERT trial) and also appears in the published article [5]. When the independent assessment group replicated the SP model and corrected this error they obtained a figure of £41K per QALY (ASSERT trial) [6]. We used the actual model and software supplied by Schering Plough and obtained a figure of £42K per QALY [7] when the error was corrected. The point of our editorial therefore remains—two independent assessments identified an error in the model, corrected it and obtained similar results that substantially exceed the £30 000 per QALY that serves as an upper bound on what is typically considered cost-effective.
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 | 0.013 | 0.103 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.065 | 0.050 |
| Insufficient payload (model declined to judge) | 0.011 | 0.013 |
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".