COVID-19 pandemic. What should Physical and Rehabilitation Medicine specialists do? A clinician's perspective
Bibliographic record
Abstract
COVID-19 pandemic is rapidly spreading all over the world, creating the risk for a healthcare collapse. While acute care and intensive care units are the main pillars of the early response to the disease, rehabilitative medicine should play an important part in allowing COVID-19 survivors to reduce disability and optimize the function of acute hospital setting. The aim of this study was to share the experience and the international perspective of different rehabilitation centers, treating COVID-19 survivors. A group of Physical Medicine and Rehabilitation specialists from eleven different countries in Europe and North America have shared their clinical experience in dealing with COVID-19 survivors and how they have managed the re-organization of rehabilitation services. In our experience the most important sequelae of severe and critical forms of COVID-19 are: 1) respiratory; 2) cognitive, central and peripheral nervous system; 3) deconditioning; 4) critical illness related myopathy and neuropathy; 5) dysphagia; 6) joint stiffness and pain; 7) psychiatric. We analyze all these consequences and propose some practical treatment options, based on current evidence and clinical experience, as well as several suggestions for management of rehabilitation services and patients with suspected or confirmed infection by SARS-CoV-2. COVID-19 survivors have some specific rehabilitation needs. Experience from other centers may help colleagues in organizing their services and providing better care to their patients.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.014 | 0.011 |
| Insufficient payload (model declined to judge) | 0.007 | 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".