Meta-Review of the Quantity and Quality of Evidence for Knee Arthroplasty Devices
Bibliographic record
Abstract
INTRODUCTION: Some cardiovascular devices are licensed based on limited evidence, potentially exposing patients to devices that are not safe or effective. Research is needed to ascertain if the same is true of other types of medical devices. Knee arthroplasty is a widely-used surgical procedure yet implant failures are not uncommon. The purpose of this study was to characterize available evidence on the safety and effectiveness of knee implants. METHODS: A review of primary studies included in health technology assessments (HTA) on total (TKA) and unicompartmental knee arthroplasty (UKA) was conducted. MEDLINE, EMBASE, CINAHL, Cochrane Library and Biotechnology & BioEngineering Abstracts were searched from 2005 to 2014, plus journal tables of contents and 32 HTA web sites. Patients were aged 18 and older who underwent primary TKA or UKA assessed in cohort or randomized controlled studies. Summary statistics were used to report study characteristics. RESULTS: A total of 265 eligible primary studies published between 1986 and 2014 involving 59,217 patients were identified in 10 HTAs (2 low, 7 moderate, 1 high risk of bias). Most evaluated TKA (198, 74.5%). The quality of evidence in primary studies was limited. Most studies were industry-funded (23.8%) or offered no declaration of funding or conflict of interest (44.9%); based on uncontrolled single cohorts (58.5%), enrolled fewer than 100 patients (66.4%), and followed patients for 2 years or less (UKA: single cohort 29.8%, comparative cohort 16.7%, randomized trial 25.0%; TKA: single cohort 25.0%, comparative cohort 31.4%, randomized trial 48.6%). Furthermore, most devices were evaluated in only one study (55.3% TKA implants, 61.1% UKA implants). CONCLUSIONS: Patients, physicians, hospitals and payers rely on poor-quality evidence to support decisions about knee implants. Further research is needed to explore how decisions about the use of devices are currently made, and how the evidence base for device safety and effectiveness can be strengthened.
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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 | 0.042 | 0.122 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.026 | 0.053 |
| Bibliometrics | 0.016 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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".