Methodologic Innovation in Creating Clinical Practice Guidelines: Insights From the 2018 Society of Critical Care Medicine Pain, Agitation/Sedation, Delirium, Immobility, and Sleep Disruption Guideline Effort
Bibliographic record
Abstract
OBJECTIVES: To describe novel guideline development strategies created and implemented as part of the Society of Critical Care Medicine's 2018 clinical practice guidelines for pain, agitation (sedation), delirium, immobility (rehabilitation/mobility), and sleep (disruption) in critically ill adults. DESIGN: We involved critical illness survivors from start to finish, used and expanded upon Grading of Recommendations, Assessment, Development and Evaluation methodology for making recommendations, identified evidence gaps, and developed communication strategies to mitigate challenges. SETTING/SUBJECTS: Thirty-two experts from five countries, across five topic-specific sections; four methodologists, two medical librarians, four critical illness survivors, and two Society of Critical Care Medicine support staff. INTERVENTIONS: Unique approaches included the following: 1) critical illness survivor involvement to help ensure patient-centered questions and recommendations; 2) qualitative and semiquantitative approaches for developing descriptive statements; 3) operationalizing a three-step approach to generating final recommendations; and 4) systematic identification of evidence gaps. MEASUREMENTS AND MAIN RESULTS: Critical illness survivors contributed to prioritizing topics, questions, and outcomes, evidence interpretation, recommendation formulation, and article review to ensure that their values and preferences were considered in the guidelines. Qualitative and semiquantitative approaches supported formulating descriptive statements using comprehensive literature reviews, summaries, and large-group discussion. Experts (including the methodologists and guideline chairs) developed and refined guideline recommendations through monthly topic-specific section conference calls. Recommendations were precirculated to all members, presented to, and vetted by, most members at a live meeting. Final electronic voting provided links to all forest plots, evidence summaries, and "evidence to decision" frameworks. Written comments during voting captured dissenting views and were integrated into evidence to decision frameworks and the guideline article. Evidence gaps, reflecting clinical uncertainty in the literature, were identified during the evidence to decision process, live meeting, and voting and formally incorporated into all written recommendation rationales. Frequent scheduled "check-ins" mitigated communication gaps. CONCLUSIONS: Our multifaceted, interdisciplinary approach and novel methodologic strategies can help inform the development of future critical care clinical practice guidelines.
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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 | 0.601 | 0.658 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.017 | 0.012 |
| Science and technology studies | 0.010 | 0.021 |
| Scholarly communication | 0.027 | 0.018 |
| Open science | 0.008 | 0.018 |
| Research integrity | 0.009 | 0.015 |
| Insufficient payload (model declined to judge) | 0.003 | 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; 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".