RESPONSE TO THE LETTER TO THE EDITOR REGARDING "HEALTH ECONOMIC EVALUATION OF MICROPROCESSOR AND NON-MICROPROCESSOR-CONTROLLED PROSTHETIC KNEES"
Bibliographic record
Abstract
We responded to comments by Brüggenjürgen et al. (https://doi.org/10.33137/cpoj.v8i2.46339) regarding our study on cost-effectiveness of different prosthesis types (https://doi.org/10.33137/cpoj.v8i2.45823). We explained why small effect differences lead to high ICUR values and clarify our costing approach. We emphasized the need for future studies on prosthesis life-cycle, long-term outcomes, and improved quality-of-life measures for more accurate evaluations. Article PDF Link: https://jps.library.utoronto.ca/index.php/cpoj/article/view/46486/34608 How To Cite: Bosman C.E, van der Sluis C.K, Vrieling A.H, Geertzen J.H.B, Seves B.L, Groen H. Response to the letter to the editor regarding “Health economic evaluation of microprocessor and non-microprocessor-controlled prosthetic knees". Canadian Prosthetics & Orthotics Journal. 2025; Volume 8, Issue 2, No. 7. https://doi.org/10.33137/cpoj.v8i2.46486 Corresponding Author: Professor Henk Groen, Affiliation: Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands.E-Mail: h.groen01@umcg.nlORCID ID: https://orcid.org/0000-0002-6629-318X
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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 | 0.009 | 0.073 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.032 | 0.029 |
| Insufficient payload (model declined to judge) | 0.011 | 0.008 |
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".