Bibliographic record
Abstract
LEARNING OBJECTIVES: After studying this article, the participant should be able to: 1. Recognize the various terms used in biostatistics. 2. Describe the choices that are required in designing a particular research study. 3. Understand the different types of data that may be obtained in any given study. 4. Identify which statistical tools are appropriate for evaluating the different types of data. SUMMARY: Journals of medicine and surgery, such as Plastic and Reconstructive Surgery, are filled with statistics that readers may never have learned about or once understood but soon forgot. Unfortunately, critical review of any abstract requires a thorough understanding of the tools used to evaluate study results. It also requires an evaluation of whether the tools chosen were adequate or even proper, given the study design and the questions asked. This article was conceived to highlight the major topics in biostatistics. It includes a review of common definitions, an outline of the major tests used (correctly or not) in plastic surgery abstracts, and instruction as to their proper use in scientific studies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.111 | 0.506 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.011 | 0.013 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.094 | 0.031 |
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".