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
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.015 | 0.722 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.004 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 it