‘Joining the Dots: Linking Prenatal Drug Exposure to Childhood and Adolescence’ – research protocol of a population cohort study
Bibliographic record
Abstract
INTRODUCTION: cohort study uses linked population data to understand the relationship between services, therapeutic interventions and outcomes of children with PDE. METHODS AND ANALYSIS: Information from routinely collected administrative databases was linked for all births registered in New South Wales (NSW), Australia between 1 July 2001 and 31 December 2020 (n=1 834 550). Outcomes for seven mutually exclusive groups of children with varying prenatal exposure to maternal substances of addiction, including smoking, alcohol, prescription/illicit drugs and neonatal abstinence syndrome will be assessed. Key exposure measures include maternal drug use type, maternal social demographics or social determinants of health, and maternal physical and mental health comorbidities. Key outcome measures will include child mortality, academic standardised testing results, rehospitalisation and maternal survival. Data analysis will be conducted using Stata V.18.0. ETHICS AND DISSEMINATION: Approvals were obtained from the NSW Population and Health Services Research Ethics Committee (29 June 2020; 2019/ETH12716) and the Australian Capital Territory Health Human Research Ethics Committee (11 October 2021; 2021-1231, 2021-1232, 2021-1233); and the Aboriginal Health and Medical Research Council (5 July 2022; 1824/21), and all Australian educational sectors: Board of Studies (government schools), Australian Independent Schools and Catholic Education Commission (D2014/120797). Data were released to researchers in September 2022. Results will be presented in peer-reviewed academic journals and at international conferences. Collaborative efforts from similar datasets in other countries are welcome.
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 | 0.057 | 0.045 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.030 | 0.011 |
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".