Proof of concept: all-virtual guideline development workshops using GRADE during the COVID-19 pandemic
Bibliographic record
Abstract
The Grading of Recommendations Assessment, Development and Evaluation (GRADE) framework is an approach to assessing the certainty of evidence and developing clinical practice recommendations based on a systematic review of the relevant literature.1 Since 2014, the US GRADE Network (USGN) has held a total of 16 semiannual guideline development workshops for attendees ranging from healthcare organisation staff to patients to guideline panel members. Using an in-person format in different cities of the continental USA, experienced educators with extensive methodological background have taught participants how to apply the GRADE approach. In October 2020, the COVID-19 pandemic forced us to shift to a virtual format. Since that time, we have held a total of three online workshops, which provided us with the unique opportunity to compare the experiences of in-person and virtual participants. As part of a routine quality improvement effort and based on our retrospective analysis of post-workshop feedback surveys, we compared attendees’ self-perceived understanding of GRADE, determined how the virtual format affected those participants’ ability to attend, and assessed whether their perceived level of engagement and interaction was similar when compared with that of in-person participants. We present our experience in accordance with the Guideline for Reporting Evidence-based practice Educational interventions and Teaching.2 The learning objectives were as follows: After completing the workshop, participants should be able to:
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
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Metaresearch Domain: Methods · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | Metaresearch Domain: Methods · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Other design | medium |
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 | 0.089 | 0.270 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.101 | 0.018 |
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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".