Impact of Patient Race on Receiving Head CT during Blunt Head Injury Evaluation
Bibliographic record
Abstract
OBJECTIVES: Prior evidence suggests that physicians may alter process of care based on race/ethnicity. The objective of this study was to determine whether race/ethnicity predicts whether a patient receives computed tomography of the head (head CT) during evaluation of blunt head injury. METHODS: This was a nonconcurrent cohort study set in an emergency department of a Level 1 trauma center in a university medical center. Consecutive patients presenting with blunt head injury from January 2000 to December 2000 were enrolled. The main outcome measure was whether or not a patient received head CT during evaluation of blunt head injury. RESULTS: The unadjusted probability of receiving head CT was similar among minority (33.9%; 95% confidence interval [CI] = 30.0% to 38.1%) and non-Hispanic white patients (36.4%; 95% CI = 33.5% to 39.3%). After adjusting for important clinical and socioeconomic predictors, minority patients had a probability of receiving head CT 0.84 times as high as that of non-Hispanic whites, but this result was not statistically significant (95% CI = 0.67 to 1.09). CONCLUSIONS: Minority and non-Hispanic white patients may not have significantly different rates of receiving head CT during evaluation of blunt head injury. A multicenter prospective study is necessary to confirm these preliminary findings.
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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".