Canadian Benchmarks in Trauma
Bibliographic record
Abstract
BACKGROUND: Benchmarks are used in trauma care for program evaluation, quality improvement, and research. National outcome benchmarks relevant to the Canadian trauma population need to be defined for evaluation of trauma care in Canada. The purpose of this study was to derive survival probabilities associated with trauma diagnoses using International Classification of Diseases, Ninth Revision (ICD-9) codes. METHODS: All patients admitted to an acute care hospital with nonpenetrating trauma and submitted to the National Trauma Registry of Canada between 1994 through 2000 inclusively were included in analyses. Both inclusive and exclusive survival risk ratios (SRRs) were calculated for groups of ICD-9 injury codes between 800 to 959. RESULTS: For the study period, there were 1,003,905 and 803,776 eligible trauma patients used to calculate inclusive SRRs and exclusive SRRs, respectively. Survival probabilities for injuries are given according to ICD-9 codes. CONCLUSION: This is the first study to define national survival benchmarks for the Canadian trauma population. These results can be used to assess survival of patients using the ICISS [(ICD-9) based Injury Severity Score (ISS)] methodology. With regular updates, these data can further be developed for continual trauma outcome assessment, quality improvement, and research into trauma care in Canada.
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 | 0.013 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.011 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 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".