Costs of multiple sclerosis – extrapolation of Czech data to Polish patients
Bibliographic record
Abstract
Daria Szmurło*a, Tomasz Fundamenta, Maciej Ziobroa, Klára Kruntorádováb, Tomáš Doležalb & Cezary Głogowskica HTA Consulting, ul. Starowiślna 17/3, 31-038 Kraków, Polandb Institute of Health Economics and Technology Assessment (iHETA), ul. Vinohradská 403/17, 120 00 Prague, Czech Republicc Biogen Idec Poland Sp. z o.o., ul. Poleczki 35, 02-822 Warszawa, Poland*Author for correspondence: +48 124 218 832 +48 123 953 832 d.szmurlo@hta.pl Aims: To estimate the direct and indirect costs associated with disability due to multiple sclerosis (MS) in Poland. Methods: Recently a cost-of-illness study was conducted in the Czech Republic, involving 909 patients with different levels of disability (the COMS study). Data on resource use from this trial was extrapolated to Polish patients and combined with Polish unit costs in 2012. The mean annual costs from societal and payers perspective were calculated for patients according to EDSS. Results: The estimated mean annual cost per patient with MS from a societal perspective ranges from 6970 EUR to 26,791 EUR. Indirect costs (production loss due to early retirement, sick-leave and informal care) cover up to 70% of total costs. Conclusions: With an estimated 40-60,000 patients with MS in Poland, the disease poses a high economic burden. Indirect costs have a substantial share in these costs. A high-quality prospective study on costs is needed.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".