Comparing the Denver criteria sets for blunt trauma: a retrospective study of cases in Edmonton, Alberta
Bibliographic record
Abstract
Objective: To determine whether a more conservative Denver criterion set could reduce unnecessary CT angiography (CTA) studies when screening for blunt cerebrovascular injury (BCVI) following blunt trauma. Methods: Following ethics approval, a retrospective chart review of 447 consecutive patients undergoing emergency CTA at two large teaching hospitals was conducted to determine the presence of risk factors for each Denver criterion set. Imaging studies of adults conducted between January 2016 and June 2020 containing sufficient clinical information for accurate classification were included in the study. Specificity, sensitivity, and predictive values were calculated. A two-sided Fisher exact test was used to evaluate the association between each iteration of the Denver criteria and the presence of BCVI. Results: The specificities of the Original, Modified, and Expanded Denver criteria were 43.58%, 34.32%, and 24.85%, respectively. Positive-predictive values (PPV) followed a different trend, with respective values of 2.77%, 3.06%, and 2.78%. Sensitivity and negative-predictive values (NPV) were found to be 100% for each criterion set. Being positive for a criterion set, and the presence of BCVI, was statistically significant for the original Denver criteria (p = 0.021, n = 443), but not the modified (p = 0.100, n = 345) or expanded Denver criteria (p = 0.202, n = 333). Conclusion: Use of the modified and expanded Denver criteria leads to the overuse of cerebrovascular imaging on patients suffering blunt force trauma. Advances in knowledge: The original Denver criteria may more appropriately identify subjects for further evaluation with CTA than the current standard, while retaining diagnostic efficacy for BCVI.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".