Emergency department coding of bicycle and pedestrian injuries during the transition from ICD-9 to ICD-10
Bibliographic record
Abstract
BACKGROUND: The international classification of diseases version 10 (ICD-10) uses alphanumeric expanded codes and external cause of injury codes (E-codes). OBJECTIVE: To examine the reliability and validity of emergency department (ED) coders in applying E-codes in ICD-9 and -10. METHODS: Bicycle and pedestrian injuries were identified from the ED information system from one period before and two periods after transition from ICD-9 to -10 coding. Overall, 180 randomly selected bicycle and pedestrian injury charts were reviewed as the reference standard (RS). Original E-codes assigned by ED coders (ICD-9 in 2001 and ICD-10 in 2004 and 2007) were compared with charts (validity) and also to ICD-9 and -10 codes assigned from RS chart review, to each case by an independent (IND) coder (reliability). Sensitivity, specificity, simple, and chance-corrected agreements (κ statistics) were calculated. RESULTS: Sensitivity of E-coding bicycle injuries by the IND coder in comparison with the RS ranged from 95.1% (95% CI 86.3 to 99.0) to 100% (95% CI 94.0 to 100.0) for both ICD-9 and -10. Sensitivity of ED coders in E-coding bicycle injuries ranged from 90.2% (95% CI 79.8 to 96.3) to 96.7% (95% CI 88.5 to 99.6). The sensitivity estimates for the IND coder ranged from 25.0% (95% CI 14.7 to 37.9) to 45.0% (95% CI 32.1 to 58.4) for pedestrian injuries for both ICD-9 and -10. CONCLUSION: Bicycle injuries are coded in a reliable and valid manner; however, pedestrian injuries are often miscoded as falls. These results have important implications for injury surveillance research.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".