Bibliographic record
Abstract
Newborn screening in North America dates to the early work of Bob Guthrie in the USA. Screening programmes in both the USA and Canada began in the early 1960s, with documented programmes in both countries as early as 1962. Throughout the 1960s and 1970s, many of the screening tests that later became part of routine screening around the world were developed in US and Canadian laboratories, including tests for phenylketonuria, other inborn errors of metabolism, congenital hypothyroidism, congenital adrenal hyperplasia, and haemoglobinopathies. An automated punching machine developed in the USA facilitated screening expansion by significantly reducing sample preparation time and effort. US and Canadian programmes were leaders in applying computerized data management to newborn screening in the 1980s. In the 1990s, DNA and tandem mass spectrometry testing protocols were developed in the USA and applied to newborn screening. US programmes have continually expanded over time, while most Canadian programmes have not. With impetus from private laboratories and professional and consumer groups, many US programmes now screen for more than 50 conditions and there is increased expansion activity in Canada. NBS research in the USA is focused on improving system efficiency and translating other genetic testing to NBS, particularly where new technologies and treatment therapies exist. Although national newborn screening policies do not exist in either Canada or the USA, there are intense efforts to provide uniform access to screening nationwide in both countries. New partnerships between health professionals, consumers and politicians are benefiting the overall screening systems in both countries.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 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".