MATERNAL-FETAL MEDICINE COMMITTEE MEMBERS
Bibliographic record
Abstract
Objective: to design national guidelines instructing obstetric care providers when, and in what populations, to consider antenatal fetal testing; which testing options are available; when to choose one testing method over another; and the expected impact on perinatal morbidity and mortality. Options: clinical situations associated with an increased risk of fetal asphyxia. Outcomes: perinatal morbidity and mortality. Evidence: Medline search from 1966 to 2000 for English language articles related to: methods of antenatal testing; comparisons of antenatal testing modalities; and impact of antenatal testing methods on perinatal morbidity and mortality. A review of meta-analyses related to antenatal testing found in the Cochrane Collaboration. Values: the evidence collected was reviewed by the MaternalFetal Medicine Committee of the SOGC under the leadership of the primary author and quantified using the evaluation of evidence guidelines developed by the Canadian Task Force on the Periodic Health Exam. Benefits, harms and costs: antenatal testing in defined populations at risk for fetal asphyxia has been shown to decrease perinatal morbidity and mortality. False positive test results can be reduced by employing a hierarchy of antenatal testing methods, reducing unnecessary intervention. Cost/benefit
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 | 0.041 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.011 | 0.006 |
| Insufficient payload (model declined to judge) | 0.053 | 0.026 |
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".