Rethinking What Is Important
Bibliographic record
Abstract
BACKGROUND: Social risk factors are often less vigorously pursued in clinical assessments of infant risk than are biologic risk factors. We examined the relative importance of early social and biologic risk factors in predicting poor health and educational outcomes in children. METHODS: The study was composed of all infants born in Winnipeg, Canada, during April-December 1984, who were followed up until age 19 years (n = 4667). Predictors were 3 routinely assessed biologic risks (birth weight, gestational age, and Apgar score) and 3 prominent social factors (mother's age, parent marital status, and socioeconomic status). Outcomes were childhood hospitalization and passage of a required high school examination. Analyses included logistic regression, measures of accuracy, and population attributable risk percent (PAR%). RESULTS: Biologic and social risk factors were associated with similarly steep poor outcomes gradients. Social risk factors had similar, and in some cases stronger, measures of association and accuracy. Using biologic risk criteria alone misclassified as low-risk 65% of cohort children who had high rates of later hospitalization and examination failure. PAR% associated with social risk factors exceeded biologic risk factors in most cases (eg, hospitalization PAR% = 4.4 for offspring of teen mothers vs. 1.7 for low birth weight). CONCLUSIONS: In a population-based sample of infants followed-up through adolescence, early social risk factors were as threatening as, and more common than, routinely documented biologic risks-frequently identifying otherwise-unrecognized at-risk children. These findings together suggest that rigorous evaluation of social factors should be made a routine part of clinical assessment to more comprehensively and accurately identify infants at risk for later serious health problems and academic failure.
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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 | 0.065 | 0.155 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.008 | 0.051 |
| Scholarly communication | 0.021 | 0.037 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.011 | 0.033 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".