Bibliographic record
Abstract
The association between a mother's use of specific medications during pregnancy and lactation and neuroblastoma in her offspring was evaluated in a case-control study. Newly diagnosed cases of neuroblastoma (n=504) in the United States and Canada were identified between 1992 and 1994 at 139 hospitals affiliated with the Pediatric Oncology Group or the Children's Cancer Group clinical trial programs. One age-matched control was sampled from the community of each case by means of random digit dialing. Exposure information was ascertained retrospectively from mothers in a structured telephone interview. Odds ratios were estimated using conditional logistic regression, with adjustment for maternal sociodemographic factors. The results did not support an association between neuroblastoma and maternal exposure to diuretic agents, antiinfective agents, estrogens, progestins, sedatives, anticonvulsant drugs, or drugs that may form N-nitroso derivatives. Mothers of cases were more likely to report using medications containing opioid agonists while they were pregnant or nursing than were mothers of controls (odds ratio=2.4, 95% confidence interval: 1.3, 4.3). Specifically, more mothers of cases reported using medications containing codeine while pregnant or nursing than did mothers of controls (odds ratio=3.4, 95% confidence interval: 1.4, 8.4). This preliminary finding may be due to bias, confounding, or chance, and additional studies are needed for confirmation.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".