Evaluating interventions for informed consent for surgery (ICONS): Protocol for the development of a core outcome set
Bibliographic record
Abstract
BACKGROUND: The concept of informed consent is fundamental to medical practice. Shortcomings in the process can lead to patient complaints, litigation, unmet expectations and poor outcomes. Consent research has focused on developing tools to improve patient recall and understanding. However, the definitions, methods of measurement and timing of measurement vary widely across the studies that have been done. Although a Cochrane review has reported that many of these interventions appear to work, the high level of heterogeneity in outcome reporting prevents the identification of those interventions that work best and why they do so. It is also not clear which outcomes are most important to each party involved in the consent process and why. METHODS/DESIGN: This project will develop a core outcome set for assessing the effects of interventions aimed at improving informed consent for surgery and other invasive procedures for adult patients with the capacity to consent for themselves. We will conduct a systematic review of the qualitative and quantitative literature to identify outcomes used to date in consent research and map these into domains. A series of semi-structured key stakeholder interviews will also be used to identify relevant outcomes. These processes will produce a list of potential outcomes for assessing the effects of interventions to improve consent, which will be refined through an international Delphi survey and consensus webinars involving key stakeholders to produce the core outcome set. DISCUSSION: The ICONS study aims to develop a core outcome set for use in trials and reviews of interventions designed to improve the informed consent process for surgery and other invasive procedures. Our aim is that this core outcome set will reduce the level of selection and reporting bias in consent research and help clinicians to compare tools to improve consent.
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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 | 0.154 | 0.178 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.072 | 0.017 |
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".