An Overview of the Methodology Used to Develop Clinical Practice Guidelines for the Management of Acute and Intraoperative Spinal Cord Injury
Bibliographic record
Abstract
STUDY DESIGN: An overview of the methods used to develop clinical practice guidelines (CPGs). OBJECTIVES: Acute spinal cord injury (SCI) and intraoperative SCI (ISCI) can have devastating physical and psychological consequences for patients and their families. To date, there are several studies that have discussed the diagnostic and management strategies for both SCI and ISCI. CPGs in SCI help to distill and translate the current evidence into actionable recommendations, standardize care across centers, optimize patient outcomes, and reduce costs and unnecessary interventions. Furthermore, they can be used by patients to assist in making decisions about certain treatments and by policy makers to inform allocation of resources. The objective of this article is to summarize the methods used to develop CPGs for the timing of surgery and hemodynamic management of acute SCI, as well as the identification and treatment of ISCI. METHODS: The CPGs were developed using standards established by the Institute of Medicine (now the National Academy of Medicine), the Guideline International Network and several other organizations. Systematic reviews were conducted according to accepted methodological standards (eg, Institute of Medicine, Agency for Healthcare Research and Quality and Patient-Centered Outcomes Research Institute) in order to summarize the current body of evidence and inform the guideline development process. Protocols for each guideline were created. A multidisciplinary guideline development group (GDG) was formed that included individuals living with SCI as well as clinicians from the broad range of specialties that encounter patients with SCI: spine or trauma surgeons, critical care physicians, rehabilitation specialists, neurologists, anesthesiologists and other healthcare professionals. Individuals living with SCI were also included in the GDG. The Grading of Recommendations, Assessment, Development and Evaluations (GRADE) approach was used to rate the certainty of the evidence for each critical outcome. The "evidence to recommendation" framework was then used to translate the evidence obtained from the systematic review to an actionable recommendation. This framework provides structure when assessing the body of evidence and considers several additional factors when rating the strength of the recommendation, including the magnitude of benefits and harms, patient preferences, resource use, health equities, acceptability and feasibility. Finally, the CPGs were appraised both internally and externally. RESULTS: The results of the CPGs for SCI are provided in separate articles in this focus issue. CONCLUSIONS: Development of these CPGs for SCI followed the methodology proposed by the Institute of Medicine the Guideline International Network and the GRADE Working Group. It is anticipated that these CPGs will assist clinicians implement the best evidence into practice and facilitate shared-decision making with patients.
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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 | 0.312 | 0.415 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.008 |
| Bibliometrics | 0.027 | 0.025 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".