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Record W2896134519 · doi:10.1097/ccm.0000000000003467

Above the GRADE: Evaluation of Guidelines in Critical Care Medicine*

2018· article· en· W2896134519 on OpenAlexaff
Charles R. Sims, Matthew A. Warner, Henry T. Stelfox, Joseph A. Hyder

Bibliographic record

VenueCritical Care Medicine · 2018
Typearticle
Languageen
FieldMedicine
TopicClinical practice guidelines implementation
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsMedicineGrading (engineering)Quality of evidenceEvidence-based medicineParenteral nutritionGuidelineMEDLINEFamily medicineData extractionAlternative medicineIntensive care medicineMeta-analysisInternal medicinePathology

Abstract

fetched live from OpenAlex

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 imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.221
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.726
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.221
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.003
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.544
GPT teacher head0.646
Teacher spread0.102 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations15
Published2018
Admission routes1
Has abstractyes

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