Principles of Designing a Cohort Study in Orthopaedics
Bibliographic record
Abstract
A well-designed and executed prospective cohort study can provide high-quality evidence in the evaluation of the effectiveness of surgical interventions. In designing a cohort study to evaluate orthopaedic interventions, it is important to recognize the limitations of the design as well as the methodological features that can be incorporated to strengthen the validity of the conclusions. In this article, we discuss the importance of the appropriate selection of participants for a control group, the management of confounders, the selection of outcomes with established measurement properties (reliability, validity, and sensitivity to change), the blinded assessment of outcomes, and the impact of nonparticipants and patients lost to follow-up.
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 | 0.511 | 0.339 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.005 | 0.015 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.008 | 0.007 |
| Research integrity | 0.011 | 0.013 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".