Evidence Versus Frenzy in Robotic Total Knee Arthroplasty
Bibliographic record
Abstract
BACKGROUND: Robotic total knee arthroplasty (rTKA) has garnered increasing attention in recent years, both clinically and in the media. The purpose of this study was to compare the volume of and messaging in published randomized controlled trials (RCTs) versus media reports on the topic of rTKA. METHODS: This was a systematic review of RCTs and media articles on rTKA. PubMed, Embase, and MEDLINE were searched for RCTs; Factiva was searched for media articles. The number of publications of each type per year was recorded. Media articles were classified on the basis of their primary information source, their general tone toward rTKA, and the benefits and drawbacks of rTKA discussed. The volume, tone, and specific messaging around rTKA were compared between media articles and RCTs. RESULTS: Fifteen RCTs and 460 media articles, published between 1991 and 2023, were included. The rates of both publication types increased over time, with more rapid increases in recent years. Ninety-five percent of media publications highlighted at least 1 benefit of rTKA. The most commonly cited benefits were more precise implant positioning (82.6%) and faster recovery (28.7%). Fewer than 7% of media publications (n = 30) mentioned downsides to rTKA. Overall, 89.3% of media articles presented a favorable view of rTKA. Ninety percent of RCTs reported that rTKA significantly outperformed manual TKA in terms of component positioning. Four of 6 RCTs reported significantly longer operative times with rTKA. Most RCTs found no significant differences in functional outcomes, opioid use, or complication rates. CONCLUSIONS: The rate of publications on rTKA has increased substantially in media sources and peer-reviewed journals, with the volume of media articles far outpacing RCTs on the topic. More precise component positioning was the most consistently reported benefit of rTKA in RCTs. However, media sources also reported a range of other, less well-supported benefits, and employed overwhelmingly positive tones regarding rTKA, more so than is supported by mixed clinical results. Efforts to ensure that patients and health-care providers receive accurate and evidence-based information about new health technologies are critical. CLINICAL RELEVANCE: This study demonstrates a clear disparity between news media coverage of rTKA and the best clinical evidence available. This information can help to guide discussions between patients and surgeons regarding the use of rTKA.
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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 | 0.036 | 0.206 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.012 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".