Systematic review for economic evaluations on newborn screening for spinal muscular atrophy
Bibliographic record
Abstract
ObjectiveEvaluate the quality and cost-effectiveness of economic evaluations of newborn screening (NBS) for Spinal Muscular Atrophy (SMA).MethodsA systematic review was conducted following Cochrane Handbook guidelines and PRISMA-S checklist. From 146 identified papers, 22 were screened for full-text, and 5 were included. Studies were evaluated for quality of reporting and transparency using the CHEERs and QHES checklists. Data was extracted to inform the review.ResultsFour economic evaluations on NBS for SMA with high reporting quality were identified. Each study employed a cost-utility analysis with similar model structures, using a decision tree for screening and a Markov model for treatment outcomes. They each compared NBS with treatment vs clinical diagnosis (no screening) with treatment. Although treatment protocols of each study varied due to differences in the strategies considered and availability of treatment. All studies included a societal perspective in their analysis and considered a lifetime horizon ranging from 30 months to 100 years. Early NBS with treatment was found to be more cost-effective than late treatment in all studies with ICER values ranging from £-117,541 to $714,000 per QALY. The wide range of ICER values are due to assumptions of long-term outcomes which are still largely unknown.ConclusionNBS with treatment was found to be cost-effective by all studies when compared to no NBS and late treatment. Although there is uncertainty around long term outcomes. Future research should focus on collecting long-term efficacy and safety data and evaluating the cost-effectiveness of pre-symptomatic treatment.
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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 | 0.033 | 0.156 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.014 | 0.018 |
| Bibliometrics | 0.014 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.029 | 0.002 |
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".