Pedestrian Fatalities Associated With Halloween in the United States
Bibliographic record
Abstract
differences in ED diagnoses was determined by the adjusted F, a variant of the second-order Rao-Scott adjusted χ 2 statistic.5 Results | Among the total population of 8 080 738 participants, 51.5% were boys, 76.0% were white, and 24.0% were Hispanic.Across all ED diagnoses, boys and girls did not have statistically significant differences in prevalence rates (Table ).Across all ED diagnoses, the overall prevalence rate was 1.4% (95% CI, 1.0%-1.8%).The prevalence of any OSFED diagnosis was 0.7% (95% CI, 0.5%-1.0%).The prevalence of AN was 0.1% (95% CI, 0.0%-0.3%).No cases of BN were present in this sample.Finally, the prevalence of BED was 0.6% (95% CI, 0.4%-0.9%).Discussion | Significant sex differences were not found across ED diagnoses.Similarly, past research with adolescents aged 13 to 18 years did not find sex differences in the prevalence of AN; however, differences emerged for BN, BED, and subthreshold AN, with higher prevalence among girls.6 Taken together, sex differences in EDs may not emerge until adolescence.This is consistent with previous research demonstrating a lack of prepubertal sex differences in EDs, with elevated prevalence of EDs in girls during and after puberty.7 Limitations of the study include the exclusion of OSFED AN, which the KSADS operationally defined only by caregivers' report of emaciation, and no assessment of avoidant and restrictive food intake disorder.Although EDs are not the focus of the ABCD study, use of the KSADS as opposed to a diagnostic tool tailored to EDs is also a limitation.The low prevalence of EDs may have reduced statistical power to detect sex differences.Future researchers may wish to characterize this sample in greater detail, including assessment of race/ethnicity, body mass index, and psychiatric comorbidity.As the ABCD cohort releases additional waves of data, researchers are encouraged to harness this nationally representative, prospective data set to explore developmental risk factors for EDs.
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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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".