Why the p Value Alone Is Not Enough: The Need for Confidence Intervals in Plastic Surgery Research
Bibliographic record
Abstract
BACKGROUND: The p value is one of the most used descriptors in statistical analysis; however, when reported in isolation, it does not convey the effect size of a treatment. The reporting of confidence intervals is an essential adjunct to determine the clinical value of treatment, as it permits an assessment of the effect size. The authors assessed the reporting of confidence intervals in clinical trials within the plastic surgery literature. METHODS: The seven highest impact plastic surgery journals were screened using MEDLINE for clinical trials in the years 2006, 2009, 2012, and 2015. Studies were randomized based on a predetermined sample size, and various characteristics (e.g., Jadad quality score, reporting of statistical significance, journal impact factor, and participation of an individual with formal research training) were documented. RESULTS: Two independent reviewers analyzed 135 articles. There was substantial interrater agreement (kappa = 0.78). Although 86.7 percent of studies reported a p value, only 25.2 percent reported confidence intervals. Of all journals assessed, Plastic and Reconstructive Surgery most frequently reported confidence intervals. The quality of the studies had a median Jadad score of 2 of 5. Bivariate analysis revealed that higher Jadad score and involvement of an individual with formal research training were associated with reporting of confidence intervals. Multivariate analysis revealed similar findings, whereas journal impact factor, year of publication, and statistical significance were not correlated with confidence interval reporting. CONCLUSIONS: Confidence intervals are underreported in the plastic surgery literature. To improve reporting quality of clinical trials, results should always include the confidence intervals to avoid misinterpretation of the effect size of a statistically significant result.
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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 | 0.796 | 0.956 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.013 | 0.008 |
| Bibliometrics | 0.031 | 0.026 |
| Science and technology studies | 0.004 | 0.036 |
| Scholarly communication | 0.024 | 0.032 |
| Open science | 0.018 | 0.012 |
| Research integrity | 0.016 | 0.024 |
| Insufficient payload (model declined to judge) | 0.003 | 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".