Quantile Regression: A Statistical Tool for Out‐of‐hospital Research
Bibliographic record
Abstract
The performance of out‐of‐hospital systems is frequently evaluated based on the times taken to respond to emergency requests and to transport patients to hospital. The 90th percentile is a common statistic used to measure these indicators, since they reflect performance for most patients. Traditional regression models, which assess how the mean of a distribution varies with changes in patient or system characteristics, are thus of limited use to researchers in out‐of‐hospital care. In contrast, quantile regression models estimate how specified quantiles (or percentiles) of the distribution of the outcome variable vary with patient or system characteristics. The authors examined the performance of traditional linear regression vs. that of quantile regression to assess the association between hospital transport interval and patient and system characteristics. They demonstrate that richer inferences can be drawn from the data using quantile regression, utilizing data drawn from a study of ambulance diversion and out‐of‐hospital delay. The results demonstrate that the effect of ambulance diversion upon out‐of‐hospital transport intervals is not uniform, but is worse on the right tail of the distribution of transport intervals. In other words, ambulance diversion disproportionately affects those patients who already have longer transport intervals. Second, the distribution of transport intervals, conditional on a given set of variables, is positively skewed, and not uniformly or symmetrically distributed. The flexibility of quantile regression models makes them particularly well suited to out‐of‐hospital research, and they may allow for more relevant evaluation of out‐of‐hospital system performance.
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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 | 0.054 | 0.210 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.030 | 0.008 |
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".