Additional file 1 of Association of knee and hip osteoarthritis with the risk of falls and fractures: a systematic review and meta-analysis
Bibliographic record
Abstract
Additional file 1: Supplemental Methods. Search strategy. Table S1. The methodological quality of included studies in accordance with the Newcastle-Ottawa Scale (NOS). Table S2. Association of radiographic and symptomatic knee osteoarthritis with falls, recurrent falls, and fractures*. Table S3. Association of radiographic and symptomatic hip osteoarthritis with falls, recurrent falls and fractures*. Table S4. Association of radiographic and symptomatic knee osteoarthritis with falls, recurrent falls and fractures*. Table S5. Associations of radiographic and symptomatic hip osteoarthritis with falls, recurrent falls and fractures*. Table S6. Evaluating the effect of radiographic and symptomatic hip and radiographic osteoarthritis on vertebral fractures. Figure S1. Funnel plots for the associations of radiographic and symptomatic knee osteoarthritis with falls and fractures. a: radiographic knee osteoarthritis and falls; b: symptomatic knee osteoarthritis and fractures. Figure S2. Funnel plots for the associations of radiographic and symptomatic hip osteoarthritis with falls and fractures. c: symptomatic knee osteoarthritis and falls; d: radiographic knee osteoarthritis and fractures.
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 | 0.004 | 0.053 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.755 | 0.027 |
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".