Evidence-based Practice: Assessing the Quality of the Evidence Part I: Applied Statistics
Bibliographic record
Abstract
Providing high quality, evidence-based care to our clients requires critical review of the literature. Part I: Applied Statistics describes the basic principles of applied statistics for evaluating the statistical and clinical quality of the literature. Different research designs are used depending on the hypothesis to be tested and resources available; some designs are more powerful in deriving significant conclusions but also have limitations. Statistical significance, determined mathematically, is essential but not synonymous to clinical importance. Determination of clinical importance will vary depending on the outcome measure and its specific context. By applying some of the basic principles outlined in this paper, the clinician can better assess the merit of the methodology, data analysis, and results reported in research papers. In addition, this paper provides background information for Part II: Grading the Evidence.
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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 | 0.335 | 0.681 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.009 | 0.005 |
| Bibliometrics | 0.038 | 0.022 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.018 | 0.008 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".