National, Regional, and State Abusive Head Trauma: Application of the CDC Algorithm
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| 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 it