Improving population health or the population itself? Health technology assessment and our genetic future
Bibliographic record
Abstract
The province of British Columbia (BC), Canada is developing its first population-wide prenatal genetic screening program, known as triple-marker screening (TMS). TMS, initiated with a simple blood test, is most commonly used to screen for fetuses with the chromosomal abnormality known as Down syndrome or neural tube disorders. Women testing TMS-positive are offered diagnostic amniocentesis and, if the diagnosis is confirmed, selective second-trimester abortion. The project described in this study was initiated to address the broad range of issues arising from this testing technology and provides an example of the new type of health technology assessment (HTA) contribution emerging (and likely to become increasing necessary) in health policy development. With the advent of prenatal genetic screening programs, would-be parents gain the promise of identifying target conditions and, hence, the option of selective abortion of affected fetuses. There is considerable awareness that these developments pose challenges in every dimension (ethical, political, economic, and clinical) of the health-care environment. In the effort to construct an appropriate prenatal screening policy, therefore, administrators have understandably sought guidance from within the field of HTA. The report authors concluded that, within the restricted path open to it, the role of government is relatively clear. It has the responsibility to maintain equal access to prenatal testing, as to any other health service. It should also require maintenance of medical standards and evaluation of program performance. At the same time, policy-makers need actively to support those individuals born with disabilities and their families.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".