Top ten priorities for anesthesia and perioperative research: a report from the Canadian Anesthesia Research Priority Setting Partnership
Bibliographic record
Abstract
PURPOSE: The purpose of the Canadian Anesthesia Research Priority Setting Partnership (CAR PSP) was to identify a top ten list of shared priorities for research in anesthesia and perioperative care in Canada. METHODS: We used the methods of the James Lind Alliance to involve patients, caregivers, healthcare professionals, and researchers in determining the research priorities in Canada. In a first survey, participants submitted questions that they want research to answer about anesthesia and perioperative care. We summarized those responses into a longlist of questions. We reviewed the literature to see if any of those questions were already answered. In a second survey, participants chose up to ten questions from the longlist that they thought were most important to be answered with research. From that list, the highest ranking questions were discussed and assigned a final rank at an in-person workshop. RESULTS: A total of 254 participants submitted 574 research suggestions that were then summarized into 49 questions. Those questions were checked against the literature to be sure they were not already adequately addressed, and in a second survey of those 49 questions, participants chose up to 10 that they thought were most important. A total of 233 participants submitted their priorities, which were then used to choose 24 questions for discussion at the final workshop. At the final workshop, 22 participants agreed on a top ten list of priorities. CONCLUSION: The CAR PSP top ten priorities reflect a wide variety of priorities captured by a broad spectrum of Canadians who receive and provide anesthesia care. The priorities are a tool to initiate and guide patient-oriented research in anesthesia and perioperative care.
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 | 0.025 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.005 |
| 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".