Long-term safety of prenatal and neonatal exposure to paracetamol: a protocol for a systematic review
Bibliographic record
Abstract
INTRODUCTION: A surge in the use of paracetamol in neonates has resulted in growing concerns about its potential long-term adverse events. In this study, we conduct a systematic review of the long-term safety of prenatal and neonatal exposure to paracetamol in newborn infants. METHODS AND ANALYSIS: We will follow the Joanna Briggs Institute Manual for Evidence Synthesis and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses statements to conduct and report this review. We will conduct a systematic search of Embase, MEDLINE, Web of Science and Google Scholar for studies with data on long-term adverse events in neonates that were exposed to paracetamol in the prenatal and/or neonatal period. We will not apply language or design limitations. We will use standardised risk of bias assessment tools to perform a quality assessment of each included article. ETHICS AND DISSEMINATION: This systematic review will only involve access to publicly available data, and therefore ethical approval will not be required. The results of this study will be communicated to the target audience through peer-reviewed publication as well as other knowledge exchange platforms, such as conferences, congresses or symposia. TRIAL REGISTRATION: The protocol for this systematic review is submitted for registration to international database of prospectively registered systematic reviews (PROSPERO, awaiting registration number).
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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 | 0.106 | 0.139 |
| Meta-epidemiology (narrow) | 0.006 | 0.005 |
| Meta-epidemiology (broad) | 0.014 | 0.013 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.009 | 0.009 |
| Insufficient payload (model declined to judge) | 0.057 | 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".