Toward an all-inclusive trauma system in Central South Ontario: development of the Trauma- System Performance Improvement and Knowledge Exchange (T-SPIKE) project
Bibliographic record
Abstract
Background: There is currently no integrated data system to capture the true burden of injury and its management within Ontario's regional trauma networks (RTNs), largely owing to difficulties in identifying these patients across the multiple health care provider records. Our project represents an iterative effort to create the ability to chart the course of care for all injured patients within the Central South RTN. Methods: Through broad stakeholder engagement of major health care provider organizations within the Central South RTN, we obtained research ethics board approval and established data-sharing agreements with multiple agencies. We tested identification of trauma cases from Jan. 1 to Dec. 31, 2017, and methods to link patient records between the various echelons of care to identify barriers to linkage and opportunities for administrative solutions. Results: During 2017, potential trauma cases were identified within ground paramedic services (23 107 records), air medical transport services (196 records), referring hospitals (7194 records) and the lead trauma hospital trauma registry (1134 records). Linkage rates for medical records between services ranged from 49% to 92%. Conclusion: We successfully conceptualized and provided a preliminary demonstration of an initiative to collect, collate and accurately link primary data from acute trauma care providers for certain patients injured within the Central South RTN. Administration-level changes to the capture and management of trauma data represent the greatest opportunity for improvement.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".