Development of primary palliative care consultation program for critically ill patients based on the Ottawa decision support framework
Bibliographic record
Abstract
ObjectiveTo develop primary palliative care consultation program for critically ill patients based on the Ottawa decision support framework(ODSF).MethodsThe draft of primary palliative care consultation program for critically ill patients was constructed under the guidance of ODSF theory,based on literature reviews and preliminary survey results.The final version of primary palliative care consultation program for critically ill patients was developed through 2 rounds of expert consultations.ResultsQuestionnaire recovery rate in the first round of expert consultations was 100.0%,with 57.1% of experts providing feedback.Questionnaire recovery rate in the second round of expert consultations was 92.9%,with 15.4% of experts providing feedback.The expert authority coefficients for the 2 rounds were 0.905 and 0.898,respectively.In the second round,the coefficient of variation for item importance scores ranged from 0.00 to 0.17,and the Kendall's W was 0.149(P=0.001).The coefficient of variation for feasibility scores ranged from 0.00 to 0.19,and the Kendall's W was 0.220(P<0.001).Finally,framework for primary palliative care consultation program for critically ill patients included 3 first⁃level items,9 second⁃level items,and 24 third⁃level items was formed,along with flow for primary palliative care consultation program for critically ill patients.ConclusionsThe primary palliative care consultation program for critically ill patients was scientific,reliable,practical,and feasible.Its consultation process was progressive,and the outcome was evidence⁃based.It could provide reference for integrating critical care and palliative 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 | 0.009 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".