Where to Build the Bridge Between Evidence and Practice?
Bibliographic record
Abstract
INTRODUCTION: Treatment of patients with traumatic brain injury (TBI) should be based upon the strongest evidence to achieve optimal patient outcomes. Given the challenges, efforts involved, and delays in uptake of evidence into practice, priorities for knowledge translation (KT) should be chosen carefully. An international workshop was convened to identify KT priorities for acute and rehabilitation care of TBI and develop KT projects addressing these priorities. METHODS: An expert panel of 25 neurotrauma clinicians, researchers, and KT scientists representing 4 countries examined 66 neurotrauma research topics synthesized from 2 neurotrauma evidence resources: Evidence Based Review of Acquired Brain Injury and Global Evidence Mapping projects. The 2-day workshop combined KT theory presentations with small group activities to prioritize topics using a modified Delphi method. RESULTS: Four acute care topics and 3 topics in the field of rehabilitation were identified. These were focused into 3 KT project proposals: optimization of intracranial pressure and nutrition in the first week following TBI; cognitive rehabilitation following TBI; and vocational rehabilitation following TBI. CONCLUSION: Three high-priority KT projects were developed: the first combined 2 important topics in acute TBI management of intracranial pressure management and nutrition, and the other projects focused on cognitive rehabilitation and vocational rehabilitation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".