Creation of an Algorithm to Identify Non-traumatic Spinal Cord Dysfunction Patients in Canada Using Administrative Health Data
Bibliographic record
Abstract
Background: The lack of consensus on the best methodology for identifying cases of non-traumatic spinal cord dysfunction (NTSCD) in administrative health data limits the ability to determine the burden of disease and provide evidence-informed services. Objective: The purpose of this study is to develop an algorithm for identifying cases of NTSCD with Canadian health administrative databases using a case-based approach. Method: Data were provided by the Canadian Institute for Health Information that included all acute care hospital and day surgery (Discharge Abstract Database), ambulatory (National Ambulatory Care Reporting System), and inpatient rehabilitation records (National Rehabilitation Reporting System) of patients with neurological impairment (paraplegia, tetraplegia, and cauda equina syndrome) between April 1, 2004 and March 31, 2011. The approach to identify cases of NTSCD involved using a combination of diagnostic codes for neurological impairment and NTSCD etiology. Results: Of the initial cohort of 23,703 patients with neurological impairment, we classified 6,362 as the “most likely NTSCD” group (had a most responsible diagnosis or pre-existing diagnosis of NTSCD and diagnosis of neurological impairment); 2,777 as “probable NTSCD” defined as having a secondary diagnosis of NTSCD, and 11,179 as “possible NTSCD” who had no NTSCD etiology diagnoses but neurological impairment codes. Conclusion: The proposed algorithm identifies an inpatient NTSCD cohort that is limited to patients with significant paralysis. This feasibility study is the first in a series of 3 that has the potential to inform future research initiatives to accurately determine the incidence and prevalence of NTSCD.
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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 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.002 |
| 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".