Reliability of International Classification of Disease-9 Versus International Classification of Disease-10 Coding for Proximal Femur Fractures at a Level 1 Trauma Center
Bibliographic record
Abstract
INTRODUCTION: The Centers for Medicare & Medicaid services proposed that transitioning from the 9th to the 10th revision of the International Classification of Disease (ICD) would provide better data for research. This study sought to determine the reliability of ICD-10 compared with ICD-9 for proximal femur fractures. METHODS: Available imaging studies from 196 consecutively treated proximal femur fractures were retrospectively reviewed and assigned ICD codes by three physicians. Intercoder reliability (ICR) was calculated. Collectively, the physicians agreed on what should be the correct codes for each fracture, and this was compared with coding found in the medical and billing records. RESULTS: No significant difference was observed in ICR for both ICD-9 and ICD-10 exact coding, which were both unreliable. Less specific coding improved ICR. ICD-9 general coding was better than ICD-10. Electronic medical record coding was unreliable. Billing codes were also unreliable, yet ICD-10 was better than ICD-9. DISCUSSION: ICD-9 and ICD-10 lack reliability in coding proximal femur fractures. ICD-10 results in data that are no more reliable than those found with ICD-9. LEVEL OF EVIDENCE: Level I diagnostic.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.015 | 0.088 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".