The Canadian Hospital Injury Reporting and Prevention Program: Captured versus uncaptured injuries for patients presenting at a paediatric tertiary care centre
Bibliographic record
Abstract
OBJECTIVES: The Canadian Hospital Injury Reporting and Prevention Program (CHIRPP) is an injury surveillance program that informs prevention policy locally and nationally. It is of import that it is reflective of the underlying population. The objective of this study was to describe differences between those injuries that were captured by the program, and those that were not. METHODS: This was a retrospective chart review of patients presenting with an injury to the IWK Health Centre between January 12, 2013 and June 30, 2013. The patients (or their parents/guardians) either completed a CHIRPP form (captured injuries), or did not (non-captured). The probability of receiving a CHIRPP form was modelled using logistic regression using patients' age, gender, disposition, Canadian Triage Assessment Scale (CTAS) score and activity/event at time of injury. RESULTS: A total of 2928 patients presented with an injury during the study period. Of these, 2135 (72.9%) were captured by the CHIRPP program and 793 (27.1%) were not. Patients (or parents) not returning the form to the department (465/793, 58.6%) represented the largest source of non-capture. The likelihood of non-capture increased with increasing CTAS score, the patient being admitted, and the following events at time of injury: drugs or overdoses, self-harm and foreign body involvement. CONCLUSION: There is an under-representation of seriously injured patients by CHIRPP at the IWK. This data may underestimate the true severity of injuries. It may also under-represent injuries that involve incidents of self-harm or drugs. Effort must be expended to increase the capture rate of CHIRPP.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".