Bibliographic record
Abstract
The number of clinical studies in the field of orthopedics is overwhelmingly large and continually growing. There are several major types of research questions that researchers can answer. These are typically classified into therapy, prognosis, diagnosis, and economic questions. This chapter focuses on those studies addressing therapy, as this is generally the most common type of study in the orthopedic surgical literature. Randomized clinical trials (RCTs) can demonstrate the superiority of a new treatment over an existing standard treatment or a placebo, or they can demonstrate that a new treatment is noninferior to an established treatment. Well-designed RCTs can provide good measures of the effect of treatments administered under ideal conditions. Observational studies inform clinicians about disease etiology, natural history, prognostic factors, and sometimes treatment effectiveness. In most evidence hierarchies, well-designed systematic reviews and meta-analyses of level I evidence are at top of the pyramid, and expert opinion and anecdotal experience are at the bottom.
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 | 0.441 | 0.764 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.015 | 0.012 |
| Bibliometrics | 0.036 | 0.031 |
| Science and technology studies | 0.006 | 0.013 |
| Scholarly communication | 0.023 | 0.013 |
| Open science | 0.009 | 0.016 |
| Research integrity | 0.011 | 0.012 |
| Insufficient payload (model declined to judge) | 0.012 | 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; 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".