Expert consensus for in-hospital neurorehabilitation during the COVID-19 pandemic in low- and middle-income countries
Bibliographic record
Abstract
Background: People with neurological dysfunction have been significantly affected by the ongoing coronavirus disease 2019 (COVID-19) crisis in receiving adequate and quality rehabilitation services. There are no clear guidelines or recommendations for rehabilitation providers in dealing with patients with neurological dysfunction during a pandemic situation especially in low- and middle-income countries. The objective of this paper was to develop consensus-based expert recommendations for in-hospital based neurorehabilitation during the COVID-19 pandemic for low- and middle-income countries based on available evidence. Methods: A group of experts in neurorehabilitation consisting of neurologists, physiotherapists and occupational therapists were identified for the consensus groups. A scoping review was conducted to identify existing evidence and recommendations for neurorehabilitation during COVID-19. Specific statements with level 2b evidence from studies identified were developed. These statements were circulated to 13 experts for consensus. The statements that received ≥80% agreement were grouped in different themes and the recommendations were developed. Results: 75 statements for expert consensus were generated. 72 statements received consensus from 13 experts. These statements were thematically grouped as recommendations for neurorehabilitation service providers, patients, formal and informal caregivers of affected individuals, rehabilitation service organizations, and administrators. Conclusions: The development of this consensus statement is of fundamental significance to neurological rehabilitation service providers and people living with neurological disabilities. It is crucial that governments, health systems, clinicians and stakeholders involved in upholding the standard of neurorehabilitation practice in low- and middle-income countries consider conversion of the consensus statement to minimum standard requirements within the context of the pandemic as well as for the future.
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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 | 0.182 | 0.335 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.006 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.007 | 0.010 |
| Research integrity | 0.010 | 0.007 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".