Mapping 3 procedure coding systems to the International Classification of Health Interventions (ICHI): coverage and challenges
Bibliographic record
Abstract
OBJECTIVE: To study the coverage and challenges in mapping 3 national and international procedure coding systems to the International Classification of Health Interventions (ICHI). MATERIALS AND METHODS: We identified 300 commonly used codes each from SNOMED CT, ICD-10-PCS, and CCI (Canadian Classification of Health Interventions) and mapped them to ICHI. We evaluated the level of match at the ICHI stem code and Foundation Component levels. We used postcoordination (modification of existing codes by adding other codes) to improve matching. Failure analysis was done for cases where full representation was not achieved. We noted and categorized potential problems that we encountered in ICHI, which could affect the accuracy and consistency of mapping. RESULTS: Overall, among the 900 codes from the 3 sources, 286 (31.8%) had full match with ICHI stem codes, 222 (24.7%) had full match with Foundation entities, and 231 (25.7%) had full match with postcoordination. 143 codes (15.9%) could only be partially represented even with postcoordination. A small number of SNOMED CT and ICD-10-PCS codes (18 codes, 2% of total), could not be mapped because the source codes were underspecified. We noted 4 categories of problems in ICHI-redundancy, missing elements, modeling issues, and naming issues. CONCLUSION: Using the full range of mapping options, at least three-quarters of the commonly used codes in each source system achieved a full match. For the purpose of international statistical reporting, full matching may not be an essential requirement. However, problems in ICHI that could result in suboptimal maps should be addressed.
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 | 0.059 | 0.217 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.013 | 0.027 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".