Delaying and Resuming Hip and Knee Arthroplasty Surgery during Covid-19 Outbreak: A Systematic Review for Solving this Challenge
Bibliographic record
Abstract
Purpose: Given that major orthopedic surgeries can be associated with worsening outcomes, it is not yet clear whether such surgeries should be a priority or postponed as much as possible in Covid-19 outbreak.The present review study tries to provide a reliable and acceptable answer to this question by comprehensively evaluating the available evidence, and finally, to provide a good summary of the results of the studies with the approach to hip and knee arthroplasty surgery.Methods: Five databases including PubMed, Web of knowledge, Google scholar, EMBASE and SCOPUS were searched using the relevant keywords by two blinded researchers.The risk bias in eligible studies was assessed by two authors based on the nine-star Newcastle-Ottawa Scale scoring system.Results: Fourteen articles were eligible for the final analysis that published between August and October 2020.With respect to early or delayed hip and knee arthroplasty surgery, we are faced with the triangle of delaying the procedure, the early or delayed patients' discharge after surgery and rescheduling the procedure as soon as possible that patient safety, patient prioritization, patient perspective and financial challenges are in the center of gravity of this triangle. Conclusion:In fact, the decision to perform surgery or delay it should be made with non-individualized and multidimensional viewpoint.
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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 | 0.008 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".