Comment on: Modelling the cost effectiveness of TNF- antagonists in the management of rheumatoid arthritis: results from the British Society for Rheumatology Biologics Registry: reply
Bibliographic record
Abstract
Sir, We thank Dr Von Vollenhoven for his interest in our paper. The questions [1] he raises about our article that examined the cost–effectiveness of biologics using the British Society for Rheumatology Biologics Registry (BSRBR) [2] provide an excellent opportunity to describe why decision modelling should be used in calculating a majority of cost–effectiveness ratios. We refer to an article by Sculpher et al. [3], which eloquently explains many of these points in detail. It should be noted that while the title of the paper refers to trials, much of its content is applicable to a registry like the BSRBR. We summarize the key points: It is for these reasons we used the BSRBR to inform parameters for a specific decision problem. Using the ‘actual’ data whereby only the BSRBR data is utilized, would simply not provide an answer to the question posed by NICE. Unless models are used to extrapolate relatively short-term data to long-term outcomes the potential benefits of many rheumatological interventions may be underestimated. This may mean that, when compared with treatments for other diseases that have more immediate effects (e.g. cancer survival), they do not appear as worthy a use of healthcare resources.
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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 | 0.018 | 0.136 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.006 | 0.002 |
| Research integrity | 0.035 | 0.044 |
| Insufficient payload (model declined to judge) | 0.009 | 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".