Centers for Disease Control and Prevention Injury Research Agenda: Identification of Acute Care Research Topics of Interest to the Centers for Disease Control and Prevention???National Center for Injury Prevention and Control
Bibliographic record
Abstract
BACKGROUND: The purpose of this report is to identify the most important research questions pertaining to the acute care of the injured patient using a Web-based Delphi technique to achieve expert opinion consensus. METHODS: Experts in trauma care from the United States and Canada (n =39) generated structured research questions and then ranked these questions in order of importance, using a Web-based survey for question generation, question ranking, and a Delphi technique of consensus. Guidelines for question construction and ranking specified that participants considered questions that fall within the interest and domain of the Centers for Disease Control (CDC)-National Center for Injury Prevention and Control (NCIPC). RESULTS: One hundred thirty-seven questions in 18 distinct categories of interest were initially generated. After two rounds of merging, collating, reassessing, and ranking by significance and importance, 25 research questions were deemed most important and significant in the care of the injured patient. Ten of these (40%) were considered to be appropriate issues for the CDC-NCIPC to address and fund, dealing with injury prevention strategies, trauma systems design and funding, the epidemiology of injury, and global outcome determinants. These 25 questions were also reviewed with consideration given to the most likely source of federal funding of investigations. CONCLUSION: This report identifies the areas of trauma care in which research efforts might best be directed. Fully 40% of the key research questions could be considered to fall under the interest and auspices of the CDC-NCIPC. The remaining questions cover a broad range of topics and likely funding sources, emphasizing the need for a coordinated oversight of research funding in trauma 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 | 0.185 | 0.246 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.015 | 0.016 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.009 | 0.010 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".