Above the GRADE: Evaluation of Guidelines in Critical Care Medicine*
Bibliographic record
Abstract
OBJECTIVES: We examined recommendations within critical care guidelines to describe the pairing patterns for strength of recommendation and quality of evidence. We further identified recommendations where the reported strength of recommendation was strong while the reported quality of evidence was not high/moderate and then assessed whether such pairings were within five paradigmatic situations offered by Grading of Recommendations Assessment, Development and Evaluation methodology to justify such pairings. DATA SOURCES AND EXTRACTION: We identified all clinical critical care guidelines published online from 2011 to 2017 by the Society of Critical Care Medicine along with individual guidelines published by Surviving Sepsis Campaign, Kidney Disease Improving Global Outcomes, American Society for Parenteral and Enteral Nutrition, and the Infectious Disease Society of America/American Thoracic Society. DATA SYNTHESIS: In all, 15 documents specifying 681 eligible recommendations demonstrated variation in strength of recommendation (strong n = 215 [31.6%], weak n = 345 [50.7%], none n = 121 [17.8%]) and in quality of evidence (high n = 41 [6.0%], moderate n = 151 [22.2%], low/very low n = 298 [43.8%], and Expert Consensus/none n = 191 [28.1%]). Strength of recommendation and quality of evidence were positively correlated (ρ = 0.66; p < 0.0001). Of 215 strong recommendations, 69 (32.1%) were discordantly paired with evidence other than high/moderate. Twenty-two of 69 (31.9%) involved Strong/Expert Consensus recommendations, a category discouraged by Grading of Recommendations Assessment, Development and Evaluation methodology. Forty-seven of 69 recommendations (68.1%) were comprised of Strong/Low or Strong/Very Low variation requiring justification within five paradigmatic scenarios. Among distribution in the five paradigmatic scenarios of Strong/Low and Strong/Very Low recommendations, the most common paradigmatic scenario was life threatening situation (n = 20/47; 42.6%). Four Strong/Low or Strong/Very Low recommendations (4/47; 8.5%) were outside Grading of Recommendations Assessment, Development and Evaluation methodology. CONCLUSIONS: Among a large, diverse assembly of critical care guideline recommendations using Grading of Recommendations Assessment, Development and Evaluation methodology, the strength of evidence of a recommendation was generally associated with the quality of evidence. However, strong recommendations were not infrequently made in the absence of high/moderate quality of evidence. To improve clarity and uptake, future guideline statements may specify why such pairings were made, avoid such pairings when outside of Grading of Recommendations Assessment, Development and Evaluation criteria, and consider separate language for Expert Consensus recommendations (good practice statements).
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 | 0.005 | 0.221 |
| 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 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".