Cost effectiveness of teratology counseling - the Motherisk experience.
Bibliographic record
Abstract
BACKGROUND: While the benefits of evidence-based counseling to large numbers of women and physicians are intuitively evident, there is an urgent need to document that teratology counseling, in addition to improving the quality of life of women and families, also leads to cost saving. The objective of the present study was to calculate the cost effectiveness of the Motherisk Program, a large teratology information and counseling service at The Hospital for Sick Children and the University of Toronto. METHODS: We analyzed data from the Motherisk Program on its 2012 activities in two domains: 1) Calculation of cost-saving in preventing unjustified pregnancy terminations; and 2) prevention of major birth defects. Cost of pregnancy termination and lifelong cost of specific birth defects were identified from primary literature and prorated for cost of living for the year 2013. RESULTS: Prevention of 255 pregnancy terminations per year led to cost savings of $516,630. The total estimated number of major malformations prevented by Motherisk counseling in 2012 was 8.41 cases at a total estimated cost of $9,032,492. CONCLUSIONS: With an estimated minimum annual prevention of 8 major malformations, and numerous unnecessary terminations of otherwise- wanted pregnancies, a cost saving of $10 million can be calculated. In 2013 the operating budget of Motherisk counseling totaled $640,000. Even based on the narrow range of activities for which we calculated cost, this service is highly cost- effective. Because most teratology counseling services are operating in a very similar method to Motherisk, it is fair to assume that these results, although dependent on the size of the service, are generalizable to other countries.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".