Delaying and Resuming Hip and Knee Arthroplasty Surgery during Covid-19 Outbreak: A Systematic Review for Solving this Challenge
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it