Cost-effectiveness modelling of recombinant FSH versus urinary FSH in assisted reproduction techniques in the UK
Bibliographic record
Abstract
BACKGROUND: The purpose of this study was to undertake an economic evaluation to compare the cost-effectiveness of recombinant (r)FSH with urinary (u)FSH for attaining clinical pregnancy with assisted reproduction. METHODS: Mathematical modelling was utilized incorporating a Markovian decision framework and a Monte Carlo simulation. Statistical representations of recurrent events over time were incorporated into a decision analysis involving fresh and frozen cycles in any sequence (after the first fresh embryo transfer cycle) over three successive assisted reproduction attempts. The mean values of transition probabilities were derived from randomized controlled clinical trials and published reports. The distributions of these transition probabilities were agreed upon by a panel of experts. Cost data for procedures and drugs were derived and validated according to the perspectives of the National Health Service and private clinics in the UK. RESULTS: The study involved 5000 Monte-Carlo simulations of treatment on a Markov cohort of 100 000 patients. The total number of pregnancies attained was significantly higher in the rFSH (40 575) compared with the uFSH (37 358) group. The cost per successful pregnancy was significantly lower for rFSH (5906 pounds sterling) compared with uFSH (6060 pounds sterling) and overall, fewer cycles of treatment were required with rFSH to achieve an ongoing pregnancy. The incremental cost-effectiveness ratio is 4148 pounds sterling for each additional clinical pregnancy with rFSH. CONCLUSIONS: In addition to the increased effectiveness of rFSH in ART, this study demonstrated that it is more cost-effective and more efficient than uFSH in attaining an ongoing pregnancy.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".