Determining the Severity of Neonatal Abstinence Syndrome Among Newborns Exposed to Selective Serotonin Reuptake Inhibitors and Serotonin and Norepinephrine Reuptake Inhibitors in Utero: A Protocol for a Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Introduction: Depression among expectant adults is increasing. This may contribute to newborns experiencing withdrawal symptoms following in utero exposure to antidepressants. Selective serotonin reuptake inhibitors (SSRIs) and serotonin and norepinephrine reuptake inhibitors (SNRIs) are the most commonly prescribed classes of antidepressants. Newborns with Neonatal Abstinence Syndrome (NAS) following fetal opioid exposure receive systematic screening and pharmacological treatment based on results from the Finnegan Neonatal Abstinence Scoring System (FNASS). In contrast, newborns exposed to SSRIs/SNRIs may not receive routine screening, thus SSRI-/SNRI-induced NAS may go undiagnosed and untreated, due to the lack of awareness of the consequences of SSRI-SNRI exposure in utero. Methods: We will search electronic databases (Ovid MEDLINE, Ovid Embase, The Cochrane Central Register of Controlled Trials, ClinicalTrials.gov, and grey literature) from inception to July 2021 for relevant articles. Two independent reviewers will eliminate duplicate articles, screen to select relevant articles, extract quantitative data pertaining to FNASS scores and assess for risks of biases. Results: Upon conducting this systematic review, we hypothesize the results will support our objective of determining the prevalence of NAS among neonates exposed to SSRIs/SNRIs in utero. Majority of neonates diagnosed with moderate to severe opioid-induced NAS receive systematic screening and pharmacological treatment. In contrast, neonates exposed to SSRIs/SNRIs may not be systematically screened, and so go on to be untreated. This is concerning considering the potential adverse effects related to untreated NAS Discussion: We plan to use the results of our meta-analysis to yield a summary of average FNASS scores in neonates exposed to SSRIs or SNRIs in utero (primary outcome). In addition, we aim to compare the resulting FNASS summary score against the proportion of neonates who received pharmacological treatment for NAS (secondary outcome). Conclusion: In conducting our proposed systematic review, we aim to determine the severity of SSRI-/SNRI-induced NAS using the FNASS scoring tool, the prevalence of newborns who receive pharmacological treatment for this condition, and to emphasize the development of standardized evidence-based guidelines for the treatment of newborns with SSRI-/SNRI-induced NAS.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.017 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.000 |
| Bibliometrics | 0.002 | 0.009 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".