Intracluster correlation coefficient in multicenter childhood trauma studies
Bibliographic record
Abstract
OBJECTIVE: To calculate the intracluster correlation coefficient (ICC) for emergency department (ED) shock rate, early trauma death (ie, death during the first 24 h after arrival at hospital), and in-hospital trauma death rate for multicenter childhood injuries. METHODS: The National Trauma Data Bank (5th revision), the largest multicenter trauma registry in the US, was used. Data from 80 trauma centers were used to calculate the ICC for in-hospital trauma death rate. Thirty three states provided data for calculation of the ICC for ED shock and early trauma death rate. RESULTS: From 2000 to 2004, 13% of the 952 242 patients in the National Trauma Data Bank were <15 years old. Approximately 17 000 of these children had injuries with an injury severity score >15, of whom 84% (14 095 subjects) were hospitalized at 80 level I or II trauma centers in 33 states. The ICCs for ED shock rate, early trauma death rate, and in-hospital death rate were 0.005 (95% CI 0.000 to 0.010), 0.014 (95% CI 0.004 to 0.024), and 0.023 (95% CI 0.013 to 0.033), respectively. These ICCs were calculated for boys and girls and also for blunt and penetrating injuries. CONCLUSION: Clustered childhood trauma studies that aim to compare different aspects of pre-hospital and hospital trauma care should incorporate these ICCs for sample calculation. When cluster randomized clinical trials are mounted, if sample sizes are calculated without adjustment for ICC, then the planned trial is likely to be seriously underpowered.
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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 | 0.124 | 0.319 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".