Resuming elective hip and knee arthroplasty after the first phase of the SARS-CoV-2 pandemic: the European Hip Society and European Knee Associates recommendations
Bibliographic record
Abstract
PURPOSE: The Covid-19 pandemic has disrupted health care systems all over the world. Elective surgical procedures have been postponed and/or cancelled. Consensus is, therefore, required related to the factors that need to be in place before elective surgery, including hip and knee replacement surgery, which is restarted. Entirely new pathways and protocols need to be worked out. METHODS: A panel of experts from the European Hip Society and European Knee Association have agreed to a consensus statement on how to reintroduce elective arthroplasty surgery safely. The recommendations are based on the best available evidence and have been validated in a separate survey. RESULTS: The guidelines are based on five themes: modification and/or reorganisation of hospital wards. Restrictions on orthopaedic wards and in operation suite(s). Additional disinfection of the environment. The role of ultra-clean operation theatres. Personal protective equipment enhancement. CONCLUSION: Apart from the following national and local guidance, protocols need to be put in place in the patient pathway for primary arthroplasty to allow for a safe return.
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 | 0.023 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.012 | 0.010 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".