Comparison and Validity of Procedures Coded With ICD-9-CM and ICD-10-CA/CCI
Bibliographic record
Abstract
BACKGROUND: The use of health administrative data in health services research is facilitated by standardized classification systems, such as the International Classification of Diseases (ICD). Canada, among other countries, recently introduced the tenth version of ICD and its accompanying Canadian Classification of Interventions (CCI). It is imperative to assess errors that could occur in administrative data due to the introduction of the new coding system. OBJECTIVE: To evaluate the validity of procedure coding in hospital discharge data, comparing CCI with ICD-9-CM. RESEARCH DESIGN: Trained reviewers examined 4008 randomly selected charts from 4 teaching hospitals in Alberta, Canada, for the presence of 30 procedures. The charts, already coded using CCI, were recoded using ICD-9-CM. Comprehensive lists of procedure codes in both systems were identified using literature, health records technicians, surgeons and online resources. MEASURES: Three databases were created for the same hospital discharge record, including CCI, ICD-9-CM, and chart review data. Sensitivity, specificity, positive predictive value, negative predictive value and kappa scores were calculated. RESULTS: Compared with the chart review data, ICD-9-CM data under-reported 17 procedures, over-reported 12, and equivalently reported 1. CCI data under-reported 19 procedures, over-reported 9, and equivalently reported 2. Kappa value was within 0.1 difference between ICD-9-CM and CCI for 14 procedures. CONCLUSIONS: Both ICD-9-CM and CCI coded the more major or invasive procedures reasonably well, but were not valid for less invasive or minor procedures. CCI can be used by health services and population health researchers with as much confidence as ICD-9-CM.
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 | 0.028 | 0.155 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".