Maternal opioid use disorder and neonatal abstinence syndrome in northwest Ontario: a 7-year retrospective analysis.
Bibliographic record
Abstract
INTRODUCTION: Opioid use in pregnancy is increasing globally. In northwest Ontario, rates of neonatal abstinence syndrome (NAS) are alarmingly high. We sought to document the increasing rates of opioid exposure during pregnancy and associated cases of NAS over a 7-year period in northwest Ontario. METHODS: We conducted a retrospective chart review at the Sioux Lookout Meno Ya Win Health Centre catchment area (population 29 000) maternity program in northwest Ontario of mother-infant dyads of live births from Jan. 1, 2009, to Dec. 31, 2015. The Integrated Pregnancy Program provides maternal, neonatal and addiction care for obstetrical patients at the health centre. We collected data on prenatal opioid exposure due to illicit and opioid agonist therapy (OAT) from patient/prescription histories and urine toxicology reports. Rates of NAS (diagnosed as a Finnegan score > 7) were recorded retrospectively from neonatal hospital charts. RESULTS: < 0.001). CONCLUSION: Despite our continually increasing rates of opioid exposure in pregnancy, rates of NAS decreased annually and were substantially lower than those of our regional LHIN. In contrast to 2009, most opioid exposure in our region is now iatrogenic as a result of OAT. These improvements may be attributable in part to the rural community-based prenatal and addictions services developed in our catchment area.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".