“Impact of maternal asthma on perinatal outcomes.” F. Firoozi, C. Lemière, M-F. Beauchesne, S. Perreault, A. Forget, and L. Blais.
Post-publication record
Source: Retraction Watch, joined by DOI. OpenAlex records retraction as is_retracted, a boolean over a state space with at least four values, so it cannot express an expression of concern, a correction or a reinstatement; it reports them as false, which reads as “fine”.
Bibliographic record
Abstract
The are conflicting results concerning the impact of maternal asthma during pregnancy on perinatal outcomes. We investigated the associations between maternal asthma during pregnancy and the risk of a small-for-gestational-age (SGA) infant, a low-birth-weight (LBW) infant, and preterm birth using a large population-based cohort. A population-based cohort of 40,788 pregnancies from asthmatic and non-asthmatic females was reconstructed through the linking of three Quebec (Canada) administrative databases between 1990 and 2002. A two-stage sampling cohort design was used to collect additional information by way of a mailed questionnaire. The generalized estimation equation models were used to obtain adjusted odds ratios of SGA, LBW and preterm birth comparing asthmatic and non-asthmatic females. The cohort included 13,007 pregnancies from asthmatic and 27,781 pregnancies from non-asthmatic females. Final estimates showed that the risk of SGA (OR: 1.27, 95% CI: 1.14–1.41), LBW (OR: 1.41, 95% CI: 1.22–1.63) and preterm delivery (OR: 1.64, 95%CI:1.46–1.83) was significantly higher among asthmatic than non-asthmatic females. Mothers with asthma during pregnancy have a higher risk of having SGA, LBW, or preterm birth infants than non-asthmatic females. However, external validity might be an issue since the cohort under represents females with high socio-economic status.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".