Concordance between childhood injury diagnoses from two sources: an injury surveillance system and a physician billing claims database
Bibliographic record
Abstract
OBJECTIVES: (1) To determine the concordance between injury diagnoses (head injury (HI), probable HI, or orthopedic injury) for children visiting an emergency department for an injury using two DATA SOURCES: an injury surveillance system (Canadian Hospitals Injury Research and Prevention Program, CHIRPP) and a physician billing claims database (Regie de l'assurance maladie de Quebec, RAMQ), and (2) to determine the sensitivity and specificity of diagnostic and procedure codes in billing claims for identifying HI and orthopedic injury among children. DESIGN: In this cross sectional cohort, data for 3049 children who sought care for an injury (2000-01) were obtained from both sources and linked using the child's personal health insurance number. METHODS: The physician recorded diagnostic codes from CHIRPP were used to categorize the children into three groups (HI, probable HI, and orthopedic), while an algorithm, using ICD-9-CM diagnostic and procedures codes from the RAMQ, was used to classify children into the same three groups. RESULTS: Concordance between the data sources was "substantial" (weighted Kappa 0.66; 95% CI 0.63 to 0.69). The sensitivity of diagnostic and procedure codes in the RAMQ database for identifying HI and for orthopedic injury were 0.61 (95% CI 0.57 to 0.64) and 0.97 (95% CI 0.96 to 0.98), respectively. The specificity for identifying HI and for orthopedic injury were 0.97 (95% CI 0.96 to 0.98) and 0.58 (95% CI 0.56 to 0.63), respectively. CONCLUSION: Combining diagnostic and procedures codes in a physician billing claims database (the RAMQ database) may be a valid method of estimating injury occurrence among children.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".