Identification of idiopathic inflammatory myopathy research cohorts using international classification of disease (ICD) codes: A systematic review
Bibliographic record
Abstract
OBJECTIVE: To summarize studies validating International Classification of Disease (ICD) coding for idiopathic inflammatory myopathies (IIM) case identification, focusing on dermatomyositis (DM) and polymyositis (PM). METHODS: PubMed, Scopus, Embase, and CINAHL search was performed with a medical librarian (Prospero #CRD4202452377). Inclusion criteria required: English language, use of ICD-9 and/or -10 coding to identify cases, and clear method for validating cases. RESULTS: 3684 citations were screened by title/abstract resulting in 69 full-text publications reviewed with 11 articles meeting inclusion criteria. Of the included studies, 5 evaluated only ICD-9, 5 evaluated only ICD-10, and one study evaluated both. All but one study in the US were single center, while those in Europe (n = 4) and Canada (n = 1) were population based. Reference standards varied, including Bohan and Peter (n = 5), 2017 EULAR/ACR classification (n = 2), expert opinion (n = 3), enrollment in Rheumatology Quality Register (n = 1) and muscle biopsy (n = 1). Four studies had a positive predictive value (PPV) of 0.9 or higher; two of these studies used coding associated with inpatient admission, and another required at least 2 codes (710.3) 3 months apart. Multiple coding instances decreased the sensitivity but increased PPV. CONCLUSIONS: ICD coding is a valuable tool for identifying cases in bioinformatic research. Only a few studies describe ICD code validation for IIM research cohort construction. The highest PPV's are reported for DM and PM with the use of multiple coding instances, or those instances associated with inpatient admission.
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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 | 0.013 | 0.059 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.008 |
| Bibliometrics | 0.021 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".