Contribution of socio‐economic status on the prevalence of cerebral palsy: a systematic search and review
Bibliographic record
Abstract
AIM: The association between socio-economic status (SES) and cerebral palsy (CP) remains controversial. Preterm birth, low birthweight, and postnatal injuries are accepted mediating risk factors for CP, but the question remains whether SES confers additional risk. The aim of this study was to analyse existing knowledge on the relationship between SES and the risk of CP. METHOD: We conducted a systematic search and review of potentially relevant research relating to SES and CP published from 1980 to 2012. Heterogeneity between studies did not allow for data aggregation or meta-analysis; therefore, a narrative review was used to summarize the findings. RESULTS: Twelve studies were included in the systematic review. Of these, eight found low SES to be a risk factor for increased CP prevalence. Three studies detected statistically significant associations even after controlling for birthweight and gestational age as variables. Two of these studies also accounted for additional confounding variables (multiple births and timing of CP acquisition) and continued to detect contributory effects of SES. Linear negative correlations between CP prevalence and SES were shown by three studies. INTERPRETATION: Evidence suggests that the effect of SES on CP prevalence goes beyond that of the mediating factors preterm birth, low birthweight, and postnatal trauma. These associations were seen in area-based and, to a lesser extent, individual measures of SES. A better understanding of mediating factors is imperative in developing targeted public health intervention programmes to reduce the prevalence of CP.
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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 | 0.006 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".