The National Trauma Registry as a Canadian Spine Trauma Database: A Validation Study Using an Institutional Clinical Database
Bibliographic record
Abstract
BACKGROUND: Miscoding is a common source of error in population-based registries. Given this, we performed a validation study comparing the Canadian National Trauma Registry (NTR) data based on the 10th Revision of the International Classification of Diseases coding with clinical data from an institutional database. METHODS: All patients with acute spine trauma who were admitted to Toronto Western Hospital from May 2003 to April 2007 were included. Accuracy, sensitivity and specificity were estimated having chart data abstraction as the gold standard. RESULTS: There were 92 patients with spine trauma (50 males, 42 females; ages from 16 to 102 years). The use of the NTR as a spine trauma database has an accuracy of 87%, sensitivity of 89.8% and specificity of 25%. If the same database is considered as a spinal cord injury (complete motor injury) database, there will be a decrease in the precision with an accuracy of 32.6%, sensitivity of 81.3% and specificity of 6.7%. CONCLUSIONS: Our results indicate that the NTR may be relatively more precise when used as a database of spine trauma in comparison with its use as a spinal cord injury database. However, the low specificity suggests that the NTR should be comprehensively validated using data from the other institutions that contribute with data collection for the NTR.
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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 | 0.067 | 0.169 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 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".