Bibliographic record
Abstract
Knee replacement surgery can be performed effectively and efficiently. In fact, many of the fundamental questions about this topic have been addressed. Indeed, much of today's research focuses on refinements of the procedure, including methods to accelerate recovery, reduce morbidity, and decrease costs. This research shows that the needs of the patient, surgeon, and healthcare system can be aligned in ways that can help improve outcomes, reduce length of stay, and lower the financial burden on society.Figure. David: J. Backstein MD, MEd, FRCS(C)In 2016, our patients are aware of, and care about, many of the same issues that we, as surgeons, concern ourselves with. Patients now inquire commonly about surgical exposures, bearing surfaces, implant brands, instrument customization, methods of fixation, and postoperative protocols—again, the same issues that we spend our time thinking about. Our research reflects this. We are in the fine-tuning stage of many areas of knee arthroplasty: Avoiding perioperative pain, reducing the frequency of transfusions, and limiting functional impairment. Studies, including many in this year's proceedings, suggest that knee replacement surgery has become a smaller procedure, with a far more tolerable recovery period than ever before. However, as we also see in this issue of the proceedings, many fundamental and important challenges remain. Defining the best limb alignment for both function and implant longevity remains controversial. We have yet to eliminate the scourge of periprosthetic infection, and we continue to work on its prevention and treatment. Additionally, questions related to the best bearing surface and revision techniques remain as relevant as ever. The proceedings that follow make it is clear that while great strides have been made, knee replacement surgery in fact is not “solved problem.” In this era of reduced surgeon compensation and hospital penalties for what once were considered inevitable complications of joint replacement surgery, standardization and optimization of care pathways appear both necessary and cost-effective. But it remains our responsibility to put patients first and advocate for the surgical techniques and implants that serve each individual best over the long-term. To that end, we must continue to find better ways to best document and measure outcomes, identify and eliminate technique and implant outliers, and do so efficiently. The articles for this year's proceedings of the Knee Society were specifically selected to provide information upon which the reader can rely upon and use in a modern clinical practice. I am proud to have been involved and sincerely hope that that the findings presented here can help readers treat their patients, as well as inspire clinician-scientists to refine future research directions.
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 | 0.005 | 0.031 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.005 | 0.001 |
| Research integrity | 0.032 | 0.023 |
| Insufficient payload (model declined to judge) | 0.035 | 0.037 |
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".