ABSTRACT 683
Bibliographic record
Abstract
Background and aims: We conducted a one-day symposium to promote knowledge exchange between researchers and knowledge users at McMaster Children’s Hospital. Our objective was to identify challenges and opportunities with respect to the prevention, diagnosis, management, and rehabilitation of sepsis at our institution. Aims: The specific aims of the symposium included: 1. To have a diverse group of stakeholders attend and participate in the symposium to provide different perspectives and points of view, 2. To identify themes of common interest and agreed upon importance to catalyze development of a collaborative pediatric sepsis research agenda at our institution. Methods: Our Research Ethics Board waived the need for ethics application related to this activity. The symposium agenda was structured to facilitate knowledge exchange among the speakers and participants. A morning session included talks from different pediatric subspecialist physicians, while an afternoon session included speakers from different health professions. Panel discussions provided opportunities for open dialogue and discussion of potential areas for future research. The Canadian Institutes of Health Research provided funding to support conduct of our symposium. Results: We achieved our aim of involving a diverse group of researchers and knowledge users in our symposium. Inclusion of a sepsis survivor as a speaker highlighted the importance of the topic and the work to be undertaken. The results of symposium discussions will be collated and published following input from participants. Conclusions: A one-day symposium was effective for the purpose of knowledge exchange between researchers and knowledge users and allowed us to achieve our overall objective.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.528 | 0.243 |
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".