National, Regional, and State Abusive Head Trauma: Application of the CDC Algorithm
Bibliographic record
Abstract
OBJECTIVE: To examine national, regional, and state abusive head trauma (AHT) trends using child hospital discharge data by applying a new coding algorithm developed by the Centers for Disease Control and Prevention (CDC). METHODS: Data from 4 waves of the Kids' Inpatient Database and annual discharge data from North Carolina were used to determine trends in AHT incidence among children <1 year of age between 2000 and 2009. National, regional, and state incidence rates were calculated. Poisson regression analyses were used to examine national, regional, and state AHT trends. RESULTS: The CDC narrow and broad algorithms identified 5437 and 6317 cases, respectively, in the 4 years of KID weighted data. This yielded average annual incidences of 33.4 and 38.8 cases per 100,000 children <1 year of age. There was no statistically significant change in national rates. There were variations by region of the country, with significantly different trends in the Midwest and West. State data for North Carolina showed wide annual variation in rates, with no significant trend. CONCLUSIONS: The new coding algorithm resulted in the highest AHT rates reported to date. At the same time, we found large but statistically insignificant annual variations in AHT rates in 1 large state. This suggests that caution should be used in interpreting AHT trends and attributing changes in rates as being caused by changes in policies, programs, or the economy.
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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 | 0.010 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".