Are Electronic Health Records Sufficiently Accurate to Phenotype Rheumatology Patients With Chronic Pain?
Bibliographic record
Abstract
Chronic pain in rheumatic diseases (RDs), a symptom often neglected, is prevalent and an important contributor to poor health and reduced quality of life. Various studies report that up to a third of patients with inflammatory RDs have persistent pain, often attributed to pain sensitization, even when the underlying disease is controlled.1-3 Beyond personal suffering, chronic pain has considerable societal implications that include increased healthcare utilization and economic disadvantages. It therefore follows that chronic pain deserves to be highlighted as an important comorbidity of RDs, deserving attention, management, and further study. A reliable estimate of the prevalence of chronic pain is necessary to better understand the contributors, life impact, and treatment of RD-associated pain. Electronic health records (EHRs) are ideally poised to provide information on large numbers of unselected patients in a real-world setting with an opportunity to contribute to a better understanding of chronic pain. A critical issue is whether the information currently available in usual EHRs can sufficiently categorize patients experiencing subjective symptoms. Information collected in EHRs is mostly in real time and entered in a structured format using controlled vocabulary, including demographics, diagnostic codes (eg, International Classification of Diseases [ICD] codes), procedure codes, laboratory test results, medications, and unstructured clinical free-text data.4,5 Developed to track and manage patients, the near universal adoption of EHRs has provided researchers with the opportunity to access real-life health data that is free of the inherent biases when patients are specifically selected for study.4 EHRs have evolved to broaden the scope of an electronic medical record with inclusion of data beyond the clinical encounter, such as information on prescriptions filled, health data from other clinicians, and patient-provided data. At the most basic level, diagnostic codes provide estimates of disease prevalence, but a more detailed study … Address correspondence to Dr. M.A. Fitzcharles, Montreal General Hospital, McGill University Health Centre, 1650 Cedar Ave, Montreal, QC H3G 1A4, Canada. Email: mfitzcharles{at}sympatico.ca.
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 | 0.039 | 0.328 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".