Framing of research question using the PICOT format in randomised controlled trials of venous ulcer disease: a protocol for a systematic survey of the literature
Bibliographic record
Abstract
INTRODUCTION: Although venous ulcers have a great social and economic impact, there is a lack of evidence from randomised controlled trials (RCTs) to support appropriate management for this disease. Framing the research question using the Population; Intervention; Comparator; Outcome; Time frame (PICOT) format in RCTs can improve the quality of the research design. OBJECTIVES: To evaluate how the PICOT format is used to frame a research question in reports of RCTs of venous ulcer disease and to determine the factors associated with better adherence to the PICOT format in framing the research question. METHODS AND ANALYSES: We will conduct a systematic survey of RCTs on venous ulcers published in the National Institute of Health, PubMed database between January 2009 and May 2016. We will include all RCTs addressing therapeutic intervention for venous ulcer disease involving human subjects, and published in the English language. The selection process will be carried out in duplicate by two independent investigators. First, titles and abstracts will be screened, then full-text articles. We will examine whether the five elements of the PICOT format are used in formulating the research question and give a score between 0 and 5. The primary outcome will be the proportion of studies that have adequately reported all five PICOT elements. DISSEMINATION: This will be the first survey to assess how the PICOT format is used to frame research questions on the management of venous ulcers in reports of RCTs. On completion, this review will be submitted to a peer-reviewed biomedical journal for publication and the findings will also be presented at scientific conferences.
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 | 0.371 | 0.415 |
| Meta-epidemiology (narrow) | 0.009 | 0.008 |
| Meta-epidemiology (broad) | 0.018 | 0.022 |
| Bibliometrics | 0.021 | 0.021 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.011 | 0.014 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.020 | 0.017 |
| Insufficient payload (model declined to judge) | 0.046 | 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; 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".