Study protocol for a descriptive analysis of non-invasive prenatal testing uptake and performance in singleton and twin pregnancies using Ontario birth registry data
Bibliographic record
Abstract
INTRODUCTION: Despite the availability of funded first-tier non-invasive prenatal testing (NIPT) for twin pregnancies in Ontario, Canada, research gaps persist regarding the feasibility and effectiveness of NIPT in this demographic. This protocol documents our planned comprehensive overview of twin data from the large Ontario provincial registry and evaluates the performance of NIPT among singleton and twin pregnancies. METHODS AND ANALYSIS: We will conduct a descriptive study using routinely collected data housed in the Better Outcomes Registry & Network Ontario. The study population will include all singleton and twin pregnancies with an estimated date of delivery between 1 September 2016 and 31 March 2023. We will compare patient characteristics, NIPT uptake and test performance metrics (including sensitivity, specificity, positive predictive value and negative predictive value) between singleton and twin pregnancies. Subgroup analyses will be conducted, including assessment by the mode of conception, trimester of initial screening, age of the pregnant individual and eligibility for publicly funded first-tier NIPT. ETHICS AND DISSEMINATION: This study has received approval from the Research Ethics Boards of the Children's Hospital of Eastern Ontario (24/01PE) and the University of Ottawa (H-04-24-10309). Results will be disseminated through scientific conferences and publication in a peer-reviewed journal. By making our protocol and findings publicly available, we aim to establish a foundational reference for future investigations in Ontario. Additionally, we seek to support the design and implementation of further studies on NIPT in twin pregnancies in Canada and elsewhere.
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 | 0.054 | 0.100 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.080 | 0.010 |
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".