Maternal smoking during pregnancy and risk of childhood neuroblastoma: Systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Prior epidemiological studies suggest a possible association between maternal smoking during pregnancy and risk of childhood neuroblastoma. A meta-analysis was performed statistically surmising all available observational studies on this topic in order to evaluate the potential correlation of maternal smoking during pregnancy and risk of childhood neuroblastoma. METHODS: Published literature was obtained from PubMed, Embase, ISI Web of Science, and Cochrane library, and all studies were inclusive until July 2014. Data from epidemiological studies were combined using a general variance-based meta-analytic method employing 95% confidence intervals. The outcome of interest was shown as odds ratio (OR) reflecting the risk of neuroblastoma development associated with smoking while pregnant. Newcastle-Ottawa Scale was used to assess the quality of studies. RESULTS: Seven case-control studies meeting protocol specified inclusion criteria were obtained through a comprehensive literature search. These studies enrolled a total of 1909 patients and 15,683 controls. Analysis for homogeneity demonstrated that the data were heterogeneous (P < 0.05) and could be statistically combined with randomized effect model. Combining all seven reports yielded an OR of 1.28 (1.01-1.62), a statistically significant result suggesting possible association between maternal smoking during pregnancy and risk of childhood neuroblastoma development (P = 0.005). There was no association between the dosage of maternal smoking during pregnancy and risk of neuroblastoma. CONCLUSION: The available epidemiological data support a possible association between maternal smoking during pregnancy and pediatric neuroblastoma development.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".