Financial Impact of Abusive Head Trauma to Age 17
Bibliographic record
Abstract
In order to illustrate the medical and financial impact arising from the care of a severely injured victim of abusive head trauma (AHT) from incident through to adulthood, a complete review of all medical financial files for the injured child beginning at the time custody of the child was transferred to the paternal grandparents (2.5 months of age) through to age 17 was conducted. Cost data were extracted from the insurance explanation of benefits or the family's bank statements. Costs were divided into seven categories and adjusted for inflation using the gross domestic product medical care deflator. Medical costs from 2.5 months to 18 years of age totaled $261 499, with $106 607 of out‐of‐pocket expenditures by the child's paternal grandparents and $154 892 covered by insurance. Costs were highly variable from year to year as reflected in the standard deviation of annual costs of $16 877. The cost of caring for a seriously injured victim of AHT is substantial. The costs may be highly variable and unpredictable from year to year making it difficult for families to plan financially. Key Practitioner Messages The financial impact of AHT on a family can be substantial and unaffordable. The financial impact for a family varies wildly from year to year. The often unaffordability of caring for a victim of AHT limits the quality and access to care in households with lower incomes.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".