Authors' Response: More research on paracetamol is required
Bibliographic record
Abstract
In his letter to the editor, John Cannell of the Vitamin D Council proposes ideas on how paracetamol might be linked to autism spectrum disorder (ASD) in women with vitamin D deficiency. These are interesting thoughts that deserve attention. In the letter, Cannell proposes that the adverse neurodevelopmental symptoms observed in our study1 are consistent with ASD. However we would like to argue that the symptoms described in our paper could be equally relevant to other neurodevelopmental disorders such as attention deficit hyperactivity disorder or language disorders. It is correct that communication problems are a central feature of ASD, but not all children with communication problems have ASD. In fact most children with communication, behavioural or motor problems do not have ASD. Moreover a central feature of ASD is social problems. We did not find any association between paracetamol and sociability in our study. Studies involving clinical diagnoses are necessary to confirm or refute a possible connection between prenatal paracetamol exposure and ASD or other neurodevelopmental disorders. Such a future study would be possible to perform by linking the Norwegian Mother and Child Cohort study to an existing national patient registry by a personal identification number. In our study we reported symptoms not previously reported in association with paracetamol exposure during pregnancy. It is therefore important that our findings are replicated before too many implications are made. We are now seeing a new era of pharmaco-epidemiological studies focusing on long-term consequences of prenatal medication exposure. We agree with Cannell that paracetamol is one of many stressors in pregnancy that deserves more attention.
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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 | 0.002 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.031 | 0.028 |
| Insufficient payload (model declined to judge) | 0.010 | 0.008 |
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".