Identifying cases of spinal cord injury or disease in a primary care electronic medical record database
Bibliographic record
Abstract
To identify cases of spinal cord injury or disease (SCI/D) in an Ontario database of primary care electronic medical records (EMR). A reference standard of cases of chronic SCI/D was established via manual review of EMRs; this reference standard was used to evaluate potential case identification algorithms for use in the same database. Electronic Medical Records Primary Care (EMRPC) Database, Ontario, Canada. A sample of 48,000 adult patients was randomly selected from 213,887 eligible patients in the EMRPC database. N/A. Candidate algorithms were evaluated using sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and F-score. 126 cases of chronic SCI/D were identified, forming the reference standard. Of these, 57 were cases of traumatic spinal cord injury (TSCI), and 67 were cases of non-traumatic spinal cord injury (NTSCI). The optimal case identification algorithm used free-text keyword searches and a physician billing code, and had 70.6% sensitivity (61.9–78.4), 98.5% specificity (97.3–99.3), 89.9% PPV (82.2–95.0), 94.7% NPV (92.8–96.3), and an F-score of 79.1. Identifying cases of chronic SCI/D from a database of primary care EMRs using free-text entries is feasible, relying on a comprehensive case definition. Identifying a cohort of patients with SCI/D will allow for future study of the epidemiology and health service utilization of these patients.
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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".