Ethical Considerations in Total Joint Arthroplasty
Bibliographic record
Abstract
Hip and knee arthroplasty surgeries have excellent outcomes and notably improve quality of life. However, ethical issues permeate the practice of adult reconstruction, and as economics and technology evolve, these issues have become increasingly important. This article will review the currently published literature on ethical issues including industry influences, implants and instrumentations, surgical innovation, new technology adoptions, and healthcare policy-relevant issues, including patient cost sharing and bundled care programs. In addition, the direct marketing of implants from the manufacturer to the general public may falsely raise patient expectations concerning the long-term clinical outcome and performance of newer devices in the absence of long-term studies. This article will also focus on relevant contemporary ethical issues that do not necessarily have preexisting published literature or guidelines but, nonetheless, are crucial for adult reconstruction surgeons to address. These issues include access to care and challenges with orthopaedic resident and fellow education. Surgeons must understand the ethical issues that can arise in their clinical practice and how those issues affect patients. Clinicians are tasked with making the best-reasoned judgment possible to optimize their patients' outcomes. Still, the ability to standardize treatment while optimizing individual outcomes for unique patients remains a challenge.
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 | 0.012 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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".