The framing of research questions using the PICOT format in randomized controlled trials of venous ulcer disease is suboptimal: A systematic survey
Bibliographic record
Abstract
Despite several publications on venous ulcers, there is still a lack of evidence from randomized controlled trials (RCTs) to support certain treatments for patients with this disorder. Well-designed research questions using the PICOT (Population; Intervention; Comparator; Outcome; Time-frame) format in RCTs can improve the quality of research. The objectives of this study were to assess how the PICOT format is used to frame research questions in RCTs published on venous ulcer disease and to determine the factors associated with better adherence to the PICOT format. We conducted a systematic survey of RCTs on venous ulcers published in the PubMed database between January 2009 and May 2016. All RCTs published in English addressing therapeutic interventions for venous ulcer disease in human subjects were included. We examined whether the five elements of the PICOT format were used in formulating the research question and scored them between 0 and 5. The primary outcome of this systematic survey was the percentage of studies that adequately reported all five PICOT elements. Eighty-five (85) RCTs were included with median PICOT score of 3 (IQR = 1.5). Four elements of PICOT were present in 28 reports (32.9%) and only 2 RCTS (2.3%) reported all the PICOT elements. Population and intervention were often appropriately described, in (70/85) 82.4% and (83/85) 97.6% of the studies, respectively; however, comparison intervention and outcome were presented in only (53/85) 62.3% and (48/85) 56.5% of studies, respectively. Very few RCTs (7.1%; 6/85) reported the study time frame. No journal or RCT characteristics were found to be significantly associated with better reporting. Use of the PICOT format to frame research questions in RCTs published on venous ulcers is suboptimal, and our study reinforces the importance of framing a good research question to improve the design of trials and quality of evidence in venous ulcer disease.
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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 | 0.635 | 0.841 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.011 | 0.012 |
| Bibliometrics | 0.027 | 0.022 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.009 | 0.014 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".