Critical appraisal of clinical practice guidelines for the management of COVID-19: protocol for a systematic review
Bibliographic record
Abstract
BACKGROUND: In December 2019, a novel coronavirus, severe acute respiratory syndrome coronavirus 2 was identified as the cause of an acute respiratory disease, coronavirus disease 2019 (COVID-19). Given the lack of validated treatments, there is an urgent need for a high-quality management of COVID-19. Clinical practice guidelines (CPGs) are one tool that healthcare providers may use to enhance patient care. As such, it is necessary that they have access to high-quality evidence-based CPGs upon which they may base decisions regarding the management and use of therapeutic interventions (TI) for COVID-19. The purpose of the proposed study is to assess the quality of CPGs that make management or TI recommendations for COVID-19 using the AGREE II instrument. METHODS: The proposed systematic review will identify CPGs for TI use and/or the management of COVID-19. The MEDLINE, EMBASE, CINAHL, and Web of Science databases, as well as the Guidelines International Network, National Institute for Health and Clinical Excellence, Scottish Intercollegiate Guidelines Network, and the World Health Organization websites, will be searched from December 2019 onwards. The primary outcome of this study is the assessed quality of the CPGs. The quality of eligible CPGs will be assessed using the Appraisal of Guidelines, Research and Evaluation II (AGREE II) instrument. Descriptive statistics will be used to quantify the quality of the CPGs. The secondary outcomes of this study are the types of management and/or TI recommendations made. Inconsistent and duplicate TI and/or management recommendations made between CPGs will be compared across guidelines. To summarize and explain the findings related to the included CPGs, a narrative synthesis will also be provided. DISCUSSION: The results of this study will be of utmost importance to enhancing clinical decision-making among healthcare providers caring for patients with COVID-19. Moreover, the results of this study will be relevant to guideline developers in the creation of CPGs or improvement of existing ones, researchers who want to identify gaps in knowledge, and policy-makers looking to encourage and endorse the adoption of CPGs into clinical practice. The results of this review will be published in a peer-reviewed journal and presented at conferences. SYSTEMATIC REVIEW REGISTRATION: International Prospective Register for Systematic Reviews (PROSPERO)- CRD42020219944.
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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 | 0.115 | 0.197 |
| Meta-epidemiology (narrow) | 0.006 | 0.006 |
| Meta-epidemiology (broad) | 0.020 | 0.019 |
| Bibliometrics | 0.016 | 0.017 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.009 | 0.010 |
| Insufficient payload (model declined to judge) | 0.057 | 0.008 |
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".