Establishing an injury indicator for severe paediatric injury
Bibliographic record
Abstract
BACKGROUND: Routinely gathered injury data, such as hospitalisations, may be subject to variation from sources other than injury incidence. There is a need for an indicator that defines severe injury, which may be less vulnerable to fluctuations due to changes in care policies. The purpose of this study was to identify International Classification of Diseases-10 codes associated with severe paediatric injuries and to specify and validate a severe paediatric injury indicator. METHODS: Two data sets that included the ISS and the survival risk ratio were used to produce a list of diagnoses to define severe paediatric injury. The list was sent to trauma surgeons who classified each code as severe enough or not severe enough to require care in a trauma centre. The indicator was fully specified, then validated by using a different data set to validate the codes in a real-world situation. RESULTS: Sixty diagnoses were identified as representing severe paediatric injury. Following specification, the indicator was applied to an existing comprehensive data set of paediatric injuries. The decline in hospitalisation of paediatric injuries was significantly steeper for severe than non-severe injuries, suggesting that factors related to the decline in this trauma subset are unlikely to be related to changes in access or other components of trauma care delivery. CONCLUSIONS: This indicator can be used for the evaluation of trends in severe paediatric trauma and will help identify populations at risk. This research may inform policies and procedures for referrals of severe childhood injury to appropriate levels of care.
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How this classification was reachedexpand
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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".