A project to validate the GLU test for preterm birth prediction in First Nations women
Bibliographic record
Abstract
The protocol described in the present article aims to validate the GLU test, a test of mid-pregnancy vaginal microbiome, for PTB risk prediction in pregnant First Nations women. Preterm birth (PTB; birth before 37 completed weeks gestation) is associated with a higher risk of adverse neonatal outcomes. First Nations communities are affected by increasing PTB rates, highest in remote communities, reaching 23%. Being able to predict women at high risk of PTB is one of the greatest challenges of our time. No reliable clinical predictors of PTB risk currently exist, beyond a previous history. Spontaneous PTB (sPTB) is highly associated with microbial infection. Recently, a Western Australian research team developed an innovative mid-pregnancy vaginal microbial DNA test, the ‘Gardnerella, Lactobacillus, Ureaplasma’ (GLU) test, capable of predicting up to 45% of sPTB cases. However, this test has only been validated in predominantly Caucasian pregnant women. The protocol described aims to validate the GLU test in pregnant First Nations women and where applicable, make modifications to this test to improve sensitivity and specificity within this population.
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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 | 0.058 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.008 |
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".