Bibliographic record
Abstract
Large-scale screening is a well-established way to identify members of a population, such as newborn infants, affected by a specific disease before the development of clinical signs of the condition, with the objective of initiating treatment that would prevent serious disability or even death. The initiative for most population-based screening programs rests with the state, rather than with the subject to be tested, who may not even be aware that testing is being done. In most cases, consent for testing is considered to be implied unless the individual or parent specifically objects. Therefore, the state assumes responsibility for ensuring that the facilities and resources are available to provide appropriate follow-up investigation and treatment of individuals identified by the screening program. Screening newborn infants for PKU was the first, large-scale genetic screening initiative to be widely adopted in a direct attempt to ameliorate the impact of genetic disease. The principles that have evolved since its introduction have served as a model for the development of numerous subsequent newborn screening programs. For many years, the principles originally articulated by Wilson and Jungner for screening in general and later modified and expanded by Pollitt et al. with specific reference to newborn screening have been accepted as the criteria for neonatal screening. They are: The condition sought should be an important health problem. This is viewed as a combination of the incidence of the condition and the consequence for the affected individual. […]
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.053 | 0.023 |
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".