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Record W4210874851 · doi:10.1111/add.15830

Commentary on Krans <i>et al</i>.: Outcomes associated with the use of medications for opioid use disorder during pregnancy

2022· letter· en· W4210874851 on OpenAlexaff
Rose A. Schmidt

Bibliographic record

VenueAddiction · 2022
Typeletter
Languageen
FieldMedicine
TopicPrenatal Substance Exposure Effects
Canadian institutionsPublic Health OntarioUniversity of TorontoCentre for Addiction and Mental Health
Fundersnot available
KeywordsPregnancyMedicineOpioid use disorderPrenatal careNeonatal intensive care unitObstetricsPediatricsOpioidIntensive care medicineEnvironmental healthPopulationInternal medicine

Abstract

fetched live from OpenAlex

Medications for opioid use disorder (MOUD) during pregnancy substantially reduce the use of unregulated opioids and are associated with improved fetal and maternal outcomes [1]. Krans et al. [2] assessed the impact of MOUD on five perinatal outcomes and classified MOUD as the total number of exposed weeks to account for non-continuous use. While this method avoided misclassifying MOUD duration for longer than actually present, understanding longitudinal behaviours such as initiation timing and adherence is essential to fully understand the impact of MOUD. The timing of the exposure, not just the duration, may be both developmentally important (i.e. potential risks for perinatal complications differ across critical and sensitive periods of fetal development in utero) and clinically relevant (i.e. timing of MOUD is associated with other maternal behaviours such as adequate prenatal care and illicit opioid use, which also have implications for perinatal outcomes). MOUD are associated with increased health-care utilization and, conversely, later initiation of MOUD in pregnancy is associated with fewer prenatal visits [3]. Inadequate prenatal care is associated with increased risk for preterm birth, low birthweight and neonatal intensive care unit admission [4]. Although not available in their data, by not accounting for the number and timing of prenatal visits the authors may have conflated the impact of prenatal care and the effects of MOUD. While it may be assumed that women with the least MOUD exposure started treatment in the third trimester, this is not necessarily true. Although not recommended in clinical guidelines, there is increasing interest in detoxification to reduce the risk of neonatal abstinence syndrome [5]. More than half of their study population (51%) with 1–10 weeks’ MOUD exposure started treatment before conception or during the first trimester. Such people may have stabilized prior to pregnancy and intentionally tapered or engaged in medically supervised withdrawal in the first few weeks after conception. These patients would have probably maintained contact with prenatal care, which would have a positive impact on prenatal outcomes, despite the short exposure to MOUD. As a result, their findings may have underestimated the effect of late initiation of MOUD on perinatal outcomes. The use of variable-centred analyses, such as multivariable regression, which predict outcomes based on relationships between variables, has dominated much of the literature assessing prenatal exposure to MOUD [6]. These methods do not account for unobserved heterogeneity in study populations [6]. In contrast, person-centred analysis techniques such as cluster analysis and latent class modelling can be used to identify important subgroups within a larger population [7]. In recent years the field of prenatal alcohol and tobacco use have greatly benefited from such techniques to identify trajectories of exposure [8-11], and recently they have been applied to MOUD [12]. Person-centred methods may overcome some of the existing limitations in administrative data, as acknowledged as an issue by Krans et al. [2]. By defining trajectories of MOUD, we may be better able to assess the specific impact of MOUD on perinatal outcomes accounting for the nuances of this complex exposure. None. I wish to acknowledge Dr Hilary Brown for her support and mentorship. Rose A. Schmidt: Conceptualization; writing-original draft preparation; writing-reviewing and editing.

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: Commentary · Consensus signal: Commentary
Teacher disagreement score0.058
Threshold uncertainty score0.790

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.020
GPT teacher head0.256
Teacher spread0.236 · 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
GenreCommentary

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

Citations3
Published2022
Admission routes1
Has abstractyes

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