Assessing quality of reports on randomized clinical trials in nursing journals.
Bibliographic record
Abstract
BACKGROUND: Several surveys have presented the quality of reports on randomized clinical trials (RCTs) published in general and specialty medical journals. The aim of these surveys was to raise scientific consciousness on methodological aspects pertaining to internal and external validity. These reviews have suggested that the methodological quality could be improved. OBJECTIVE: We conducted a survey of reports on RCTs published in nursing journals to assess their methodological quality. The features we considered included sample size, flow of participants, assessment of baseline comparability, randomization, blinding, and statistical analysis. METHODS: We collected data from all reports of RCTs published between January 1994 and December 1997 in Applied Nursing Research, Heart & Lung and Nursing Research. We hand-searched the journals and included all 54 articles in which authors reported that individuals have been randomly allocated to distinct groups. We collected data using a condensed form of the Consolidated Standards of Reporting Trials (CONSORT) statement for structured reporting of RCTs (Begg et al., 1996). RESULTS: Sample size calculations were included in only 22% of the reports. Only 48% of the reports provided information about the type of randomization, and a mere 22% described blinding strategies. Comparisons of baseline characteristics using hypothesis tests were abusively produced in more than 76% of the reports. Excessive use and unstructured reports of significance testing were common (59%), and all reports failed to provide magnitude of treatment differences with confidence intervals. CONCLUSIONS: Better methodological quality in reports of RCTs will contribute to increase the standards of nursing research.
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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 | 0.671 | 0.910 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.010 | 0.010 |
| Bibliometrics | 0.073 | 0.050 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.005 | 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".