The Critical Care Research Network: a partnership in community‐based research and research transfer
Bibliographic record
Abstract
The objectives of this study were to present a short history of the Critical Care Research Network (CCR-Net), describe its approach to health services research and to summarize completed and current research projects. In doing this, we explored the question is this research network accomplishing its goals? We reviewed the medical literature to identify studies on similar types of Networks and also the evidence supporting the methodology used by CCR-Net to conduct research using MEDLINE, HEALTHSTAR, CINAHL and the keywords network and health care or healthcare, benchmarking and health care or healthcare, and research transfer or research utilization. We also reviewed the bibliographies of retrieved articles and our personal files. In addition, we summarized the results of studies conducted by CCR-Net and outlined those currently in progress. A review of the literature identified studies on two similar networks that appeared to be succeeding. In addition, the literature was also supportive of the general process used by CCR-Net, although the level of evidence varied. Finally, the studies conducted to date within CCR-Net follow the suggested methodology. At the time of this preliminary communication CCR-Net appears to have adopted a valid approach to health services research within the area of Critical Care Medicine. Further direct evidence is required and appropriate studies are planned.
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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 | 0.237 | 0.214 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.014 | 0.014 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.017 | 0.032 |
| Open science | 0.005 | 0.032 |
| Research integrity | 0.009 | 0.012 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".