Bibliographic record
Abstract
Randomized, controlled trials (RCTs) represent the highest level of evidence attainable. Although up to 60% of clinical questions cannot be answered by an RCT, it is still the ideal method of comparing orthopedic implants. The strength of the findings of RCTs is a result of the investigators ability to limit bias through careful attention to study design. Bias may be limited through randomization, allocation concealment, and blinding. Unfortunately, RCTs involving different surgical techniques or implants present many challenges that may introduce bias and threaten the validity of study results. The purpose of this review is to outline these issues and present possible solutions to these challenges, with the ultimate goal of improving surgeons understanding of how to design and critically appraise RCTs involving orthopedic implants.
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 | 0.721 | 0.779 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.012 | 0.009 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.005 | 0.023 |
| Scholarly communication | 0.016 | 0.014 |
| Open science | 0.008 | 0.006 |
| Research integrity | 0.032 | 0.019 |
| Insufficient payload (model declined to judge) | 0.011 | 0.006 |
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".