Fetal Fibronectin Testing in Ontario: Successful Government-Sector Collaboration to Achieve High-Quality and Sustainable System Change
Bibliographic record
Abstract
Ontario's province-wide implementation of fetal fibronectin (fFn) technology, a test to identify women unlikely to deliver within two weeks of presentation with symptoms of preterm labour, is a notable example of evidence-informed system improvement and productive government-sector partnership. Increasing demand for costly, high-risk maternal and newborn care in Ontario hospitals prompted a provincial review. Sector experts identified potentially avoidable maternal admissions and transfers to high-risk units for evaluation of suspected preterm labour as an opportunity for system improvement. Limited access to fFn testing was documented, and expert consensus posited that funding rapid clinical testing to identify women at low risk for preterm delivery would yield a significant return on investment. An expert panel recommended evidence-based clinical guidelines. The government swiftly secured funding and initiated a successful implementation strategy, capitalizing on regional perinatal networks. Amassing clinical and care utilization information, framing the data in a policy-relevant context and partnering sector expertise with ministry capability resulted in this technology being effectively implemented in a complex health system.
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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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".