Management of the Injured Patient: Identification of Research Topics for Systematic Review Using the Delphi Technique
Bibliographic record
Abstract
BACKGROUND: Systematic reviews of controlled clinical trials in the form of meta-analyses can serve as an important guide to direct clinical practice. This study identifies the most important research questions pertaining to the acute care of the injured patient using a Web-based Delphi technique to achieve consensus of expert opinion. METHODS: Experts in trauma care from the United States and Canada (n = 68) were asked to generate structured research questions and were then required to rank these questions in order of importance and estimate the amount of research currently published. RESULTS: The questions ranking in the highest tertile are presented along with an estimate of their importance and the amount of research published using an ordinal scale. Only 9 of 16 (56%) questions had some or a substantial amount of research available on which to perform a systematic review. CONCLUSION: This study identifies the areas of trauma care in which research efforts might best be directed. In the absence of sufficient data for systematic reviews, these research topics represent important areas for the design and implementation of clinical trials.
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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 | 0.370 | 0.473 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.032 | 0.015 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".