Sensitivity, specificity and predictive values of ICD-10 substance use codes in a cohort of substance use-related endocarditis patients
Bibliographic record
Abstract
Background: Healthcare databases have the potential to become efficient tools for epidemiological research in People Who Inject Drugs (PWID). The validity of ICD-10 codes for specific substances in this population has not been assessed. Objectives: Validate ICD-10 diagnosis codes relating to the use of specific substance classes in a cohort of endocarditis patients. Methods: Our study sample consisted of 379 first-episode infective endocarditis patients (Male: 208, Female: 171), aged 18–55, admitted to any of three hospitals in London, Ontario from 2007 to 2018. Of these, 287 used drugs. We validated ICD-10 substance use codes for opioids (F11), stimulants (F15), cocaine (F14) and multiple substances (F19). Sensitivity, specificity, Positive Predictive Value (PPV) and Negative Predictive Value (NPV) were calculated for each code, using self-reported substance use documented on medical record review as a gold standard. We conducted a comparative analysis between code-negative users and code-positive users for each substance. Results: All substance use codes shared the same pattern: high specificity, high PPV and low sensitivity, with code F11 yielding the highest PPV (96.3%; 95% C.I.: 90.8–98.6) and sensitivity (42.6%; 95% C.I. 36.3–49.1). The code-positives and code-negatives for each substance did not differ significantly in any characteristics compared. Conclusion: Our results suggest that the individual ICD-10 codes analyzed should not be used for research without adjustment for low sensitivity. However, due to high PPV and specificity, these codes may still have potential for research use. Because code-negative patients did not differ from code-positive patients, their data may be extrapolated to the overall group of substance users.
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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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".