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Record W2553195594 · doi:10.1111/add.13551

The comparative safety of buprenorphine versus methadone in pregnancy—what about confounding?

2016· letter· en· W2553195594 on OpenAlexaff
Susan B. Brogly, Kelley Saia, Sonia Hernández–Dı́az, Martha M. Werler, Paola Sebastiani

Bibliographic record

VenueAddiction · 2016
Typeletter
Languageen
FieldMedicine
TopicPrenatal Substance Exposure Effects
Canadian institutionsQueen's University
FundersEunice Kennedy Shriver National Institute of Child Health and Human Development
KeywordsMethadoneBuprenorphineMedicinePregnancyConfoundingOpioidObstetricsPrenatal careMethadone maintenancePediatricsAnesthesiaInternal medicinePopulationEnvironmental health

Abstract

fetched live from OpenAlex

Preferential treatment of high-risk opioid-dependent pregnant women with methadone limits evidence of the comparative safety of buprenorphine versus methadone on infant outcomes. Adjustment for maternal characteristics that affect both treatment choices and birth outcomes is necessary to provide valid estimates of the effect of prenatal opioid agonist therapy exposure. The study by Zedler et al. aimed to assess systematically evidence of the safety of prenatal buprenorphine versus methadone and to provide a quantitative treatment effect 1. Some published studies have shown less severe neonatal abstinence syndrome (NAS) 2-5 and greater gestational age at birth 4, birth weight 4, 6 and head circumference 6, 7 after prenatal buprenorphine exposure compared to prenatal methadone. What has been difficult to disentangle, however, is whether these improved birth outcomes are due to the protective effect of buprenorphine compared to methadone or to confounding from maternal treatment choices 8. Buprenorphine treatment often involves out-patient prescriptions, while methadone is given through observed daily dosing at a methadone clinic. Maternal characteristics have been shown to influence clinical prescribing, with methadone being used typically in less stable opioid-dependent pregnant women 4, 9-12. Women with poorer clinical profiles, such as those taking concomitant psychotrophic medications 2, are more likely to have neonates with worse birth outcomes than women with better clinical profiles 13. When a regression model of prenatal buprenorphine compared to methadone is unadjusted for confounding—such as differences in maternal clinical profiles by treatment choice—the estimated measure of effect (i.e. risk ratio) is a mix of both the effects of prenatal treatment and the confounder on the infant. While we commend Zedler et al. for their efforts, their publication does not clarify the available evidence. The confidence interval for their overall summary estimate would be narrower than those from the individual studies due to the reduction in random error achieved by the larger pooled sample size. This pooling of data, however, does nothing to adjust for systematic error (i.e. confounding bias, information bias, selection bias). Pooling studies that are confounded produces an overall summary estimate that also is confounded. Zedler et al. would have provided a more valid effect estimate had they attempted to remove some of the uncontrolled confounding from their pooled estimate, perhaps by bias analysis simulation 14, as was performed in our meta-analysis published in 2014 15. We showed that confounding in published cohort studies and confounding and/or selection bias in randomized controlled trials from study dropout could contribute to the observed protective effect of buprenorphine versus methadone on the neonate. Increasing rates of opioid dependence in pregnant women and of NAS in their neonates are major health issues in the United States. NAS has implications for the long-term health of the infant and is associated with soaring hospital costs and decreasing neonatal intensive care unit resources 16. Efforts are needed urgently to reduce NAS and other adverse birth outcomes in these infants. None.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.800
Threshold uncertainty score0.878

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.026
GPT teacher head0.297
Teacher spread0.271 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations7
Published2016
Admission routes1
Has abstractyes

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