143: Understanding the Pediatric Medical Literature: Which Statistical Tests Should Residents Know?
Bibliographic record
Abstract
In recent years there has been increasing emphasis on critical appraisal skills in medical training. This is reflected in both the requirements of the Royal College of Physicians and Surgeons of Canada (RCPSC) and the Accreditation Council for Graduate Medical Education (ACGME). However, little information exists to guide educators on developing relevant competencies. Research has shown that the majority of residents lack sufficient knowledge to understand data analysis in much of the published literature. We sought to determine which statistical concepts are most frequently reported in pediatric journals, in order to inform resident education in critical appraisal. We conducted a review of articles in seven pediatric journals previously reported to contain best evidence for clinical practice: (1) Archives of Diseases in Childhood, (2) British Medical Journal (BMJ), (3) Journal of the American Medical Association (JAMA), (4) Journal of Pediatrics, (5) Lancet, (6) New England Journal of Medicine, and (7) Pediatrics. For each of these publications, a single monthly issue was included (June 2009). Review of two additional months of publication is in progress. Original research articles enrolling at least one patient <18 years of age were considered eligible. Two independent reviewers abstracted relevant statistical tests and terms; disagreements were resolved by consensus. Descriptive statistics were used in determining the most common statistical tests in the relevant pediatric literature. We then determined the proportion of literature accessible to readers who understand the three most commonly reported statistical tests/procedures. A total of 102 articles met inclusion criteria. Of these, the majority were observational in design (n=88 [86%]). Fourteen studies (14%) did not use any inferential statistics, while the remainder employed at least one statistical test. The most commonly reported were the chi square test (n=37 [36%]), logistic regression (n=37 [36%]) and the t test (n=27 [26%]). A reader familiar with these three commonly reported tests would be able to understand 30% of the literature. ANOVA, Fisher's exact test, tests of correlation and linear regression were also used in more than 10% of articles. These results identify which statistical tests are most commonly used in the pediatric medical literature and therefore which tests are the most important for residents to understand. Such information could be used to 1) promote competency in critical appraisal, and 2) meet RCPSC and ACGME education guidelines.
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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 | 0.098 | 0.458 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.020 | 0.014 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.007 | 0.013 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".