Authorship in reports of clinical practice guidelines: A systematic cross-sectional analysis
Bibliographic record
Abstract
BACKGROUND: A transparent and explicit reporting on authors' contributions to the development of clinical practice guidelines and on panelists' characteristics is essential for their credibility and trustworthiness. We did not find published studies on authorship or panel involvement in clinical practice guidelines. OBJECTIVE: To describe the approach to authorship in reports of clinical practice guidelines, and the characteristics of individual authors. METHODS: We conducted a cross-sectional survey of guidelines listed in the National Guideline Clearing House (NGC) in 2016. We abstracted data on the general characteristics of the guidelines, report approach to authorship, and individual authors characteristics. Data abstraction was in duplicate and independent manner using standardised form. Data analyses were both descriptive and regression analyses. RESULTS: Overall, 139 eligible guidelines with published papers were identified. Of these, 48 (35%) included a group authorship statement in the author byline. A third of these guidelines (n = 45; 32%) reported on authors' contributions, while about half of the guidelines (n = 74; 53%) reported who of the authors served as panel members. Around one-fifth of the guidelines (n = 30; 22%) reported group membership (eg, content expert, patient representative) for at least 1 author. Less than one-seventh of the eligible guidelines indicated who selected the panel members (n = 18; 13%), reported the types of panel members (n = 18; 13%) or the selection criteria (n = 12; 9%). Higher journal impact factor was associated with both "reporting of the author contributions" (OR = 1.07) and "the inclusion of a panel membership section in the guideline report" (OR = 1.21). CONCLUSION: Low percentages of clinical practice guidelines report information on important aspects of authorship and characteristics of individual authors. Better reporting of some of these criteria was associated with journal impact factor.
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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 | 0.091 | 0.260 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.012 | 0.016 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".