Association between framing of the research question using the PICOT format and reporting quality of randomized controlled trials
Bibliographic record
Abstract
BACKGROUND: Experts recommend formulating a structured research question to guide the research design. However, the basis for this recommendation has not been formally evaluated. The aim of this study was to examine if a structured research question using the PICOT (Population, Intervention, Comparator, Outcome, Time-frame) format is associated with a better reporting quality of randomized controlled trials (RCTs). METHODS: We evaluated 89 RCTs reports published in three endocrinology journals in 2005 and 2006, the quality of reporting of which was assessed in a previous study. We examined whether the reports stated each of the five elements of a structured research question: population, intervention, comparator, outcome and time-frame. A PICOT score was created with a possible score between 0 and 5. Outcomes were: 1) a 14-point overall reporting quality score (OQS) based on the Consolidated Standards for Reporting Trials; and 2) a 3-point key score (KS), based on allocation concealment, blinding and use of intention-to-treat analysis. We conducted multivariable regression analyses using generalized estimating equations to determine if a higher PICOT score or the use of a structured research question were independently associated with a better reporting quality. Journal of publication, funding source and sample size were identified as factors associated with OQS in our previous report on this dataset, and therefore included in the model. RESULTS: A higher PICOT score was independently associated with OQS (incidence rate ratio (IRR) = 1.021, 95% CI: 1.012 to 1.029) and KS (IRR = 1.142, 95% CI: 1.079 to 1.210). A structured research question was present in 33.7% of the reports and it was associated with a better OQS (IRR = 1.095, 95% CI 1.059-1.132) and KS (IRR = 1.530, 95% CI 1.311-1.786). CONCLUSIONS: Better framing of the research question using the PICOT format is independently associated with better overall reporting quality - although the effect is small - and better reporting of key methodologies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.993 | 0.998 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.017 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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; both teacher heads 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".