Neuropathic pain in a primary care electronic health record database
Bibliographic record
Abstract
BACKGROUND: Neuropathic pain (NP) is common in the adult population but is difficult to study in electronic health record (EHR) databases because it is a symptom rather than a pathologic diagnosis. The first step in studying NP in EHR databases is to develop methods for identifying patients with NP. The objectives of this study were to develop estimates of the prevalence of NP among patients in a primary care EHR database and describe these patients' demographic characteristics and health-care utilization. METHODS: This was a retrospective cohort study of de-identified data from a 5-year period (2005-2010) from 23 general practitioners (GPs) in 10 primary care practices in southwestern Ontario, Canada. International Classification of Diseases version 9 (ICD-9) diagnostic codes and medication prescriptions were used to identify patients with certain and probable NP. RESULTS: Different methods produced prevalence estimates ranging from 1.5% (for certain NP in the epidemiologically rigorous period cohort) to 11.2% (for certain NP + probable NP in the more inclusive database cohort). Patients in the NP groups had more GP visits, specialist referrals and analgesic prescriptions than patients without NP. CONCLUSION: This study represents a step towards being able to utilize EHR databases to study NP by proposing methods to identify patients with certain and probable NP in a primary care EHR database. Validation against a gold standard is the next step.
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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 | 0.006 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".