How to Develop a Reporting Guideline
Bibliographic record
Abstract
Reporting guidelines complement journals' instructions to authors. They usually take the form of a checklist, providing structured advice on how to report research studies. This chapter summarizes the main steps in the development of evidence-based consensus guidelines for reporting health research studies. Successful development of a reporting guideline requires an executive group of three to five members to facilitate and coordinate the process. The chapter explains the developing a reporting guideline in five phases: the initial steps of developing a strong rationale for the guidance and ensuring that others have not already done so, the premeeting activities that include preparatory work required for a successful meeting, the face-to-face consensus meeting activities that enable the collaborative work of a full guideline development group, the postmeeting activities that include developing the final guidance and related documents for publication, and postpublication activities to support guideline implementation.
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: Reporting · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | Metaresearch Domain: Reporting · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Not applicable | high |
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 | 0.187 | 0.484 |
| Meta-epidemiology (narrow) | 0.002 | 0.004 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.016 | 0.008 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.014 | 0.016 |
| Open science | 0.007 | 0.006 |
| Research integrity | 0.013 | 0.019 |
| Insufficient payload (model declined to judge) | 0.027 | 0.046 |
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.
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".