A Population-Based Analysis of the Discrepancy Between Potential and Realized Access to Trauma Center Care
Bibliographic record
Abstract
In Brief Objective: To explore whether a discrepancy between the availability of trauma services (potential access) and trauma center utilization rates (realized access) exists, with the aim of informing strategies to improve access. Background: Lack of access to trauma center care has frequently been attributed to the geographic distribution of trauma centers. Alternatively, impeded access to trauma center care might be due to suboptimal triage practices in the setting of appropriate resources. Methods: Population-based retrospective cohort study of severely injured adult patients (2002–2010). Potential access to trauma center care was evaluated using network-based spatial analysis of census data and was defined as residing within 1 hour of a trauma center. Realized access to trauma center care was evaluated using population-based data sources and was defined as direct transport from the scene of injury to a trauma center. Concordance between potential and realized access (high, moderate, or low) was evaluated at the county level. Results: Of the population in the study region, 7,340,711 persons (60%) had potential access to trauma center care; persons in 11 counties (22%) had high potential access. Of 26,861 severely injured patients, 10,237 (38%) had realized access to trauma center care; patients in only 4 counties (8%) had high realized access. The concordance between potential and realized access was moderate (weighted κ = 0.49); 63% of counties (n = 7) with high potential access performed worse than expected and had moderate or low realized access. Conclusions: There is limited concordance between potential and realized access. Regions with high potential access had low realized access, and vice versa. This evaluation suggests that strategies to improve access must be based on understanding the distribution of centers and the triage practices used to access trauma care. A significant proportion of severely injured patients receive definitive care at nontrauma centers. Impediments to accessing trauma center care might be either because of limited availability on account of the geographic distribution of centers or because of suboptimal triage practices in the setting of appropriate resources. In this population-based analysis, we show that the simultaneous evaluation of the availability of trauma services and triage practices can better inform strategies to improve access to care.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".