Development of a Core Outcome Set for Studies on Cardiac Disease in Pregnancy (COSCarP): a study protocol
Bibliographic record
Abstract
BACKGROUND: Clinical studies looking at interventions to optimize pregnancy and long-term outcomes for women with cardiac disease and their babies are inconsistent in their reporting of clinical outcomes, making it difficult to compare results across studies and draw meaningful conclusions. The development of a core outcome set (COS)-a standardized, minimum set of outcomes that must be collected and reported in all studies-is a practical solution to this problem. METHODS/DESIGN: We will follow a five-step process in developing a COS for studies on pregnant women with cardiac disease. First, a systematic literature review will identify all reported outcomes (including patient-reported outcomes) and definitions. Second, semi-structured interviews with stakeholders involved in the care of pregnant women with cardiac disease will determine their perspective and add new outcomes that they consider important. Third, an international electronic Delphi survey will narrow outcomes obtained through the first two steps, in an attempt to arrive at a consensus. Fourth, a face-to-face consensus meeting will deliberate to finalize the COS. Finally, measurement tools and definitions for included outcomes will be determined through a series of literature reviews and Delphi surveys. DISCUSSION: This protocol provides an overview of the steps involved in the development of a COS that must be reported in studies involving pregnant women with cardiac disease, in an attempt to harmonize outcome reporting and ensure the validity of study results that will not only inform clinical practice and future research but also encourage the development of COS in other areas of medicine. COMET CORE OUTCOME SET REGISTRATION: http://www.comet initiative.org/studies/details/834.
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
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 | 0.243 | 0.233 |
| Meta-epidemiology (narrow) | 0.004 | 0.005 |
| Meta-epidemiology (broad) | 0.005 | 0.008 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.036 | 0.010 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".