Functional and Clinical Characteristics of Individuals Attending Pulmonary Rehabilitation After Severe COVID-19
Bibliographic record
Abstract
BACKGROUND: A proportion of patients with COVID-19 need hospitalization due to severe respiratory symptoms. We sought to analyze characteristics of survivors of severe COVID-19 subsequently admitted to in-patient pulmonary rehabilitation and identify their rehabilitation needs. METHODS: From the COVID-19 Registry of Fondazione Don Gnocchi, we extracted 203 subjects admitted for in-patient pulmonary rehabilitation after severe COVID-19 from April 2020-September 2021. Specific information on acute-hospital stay and clinical and functional characteristics on admission to rehabilitation units were collected. RESULTS: During the acute phase of disease, 168 subjects received mechanical ventilation for 26 d; 85 experienced delirium during their stay in ICU. On admission to rehabilitation units, 20 subjects were still on mechanical ventilation; 57 had tracheostomy; 142 were on oxygen therapy; 49 were diagnosed critical illness neuropathy; 162 showed modified Barthel Index < 75; only 51 were able to perform a 6-min walk test; 32 of 90 scored abnormal at Montreal Cognitive Assessment; 43 of 88 scored abnormal at Hospital Anxiety and Depression Scale; 65 scored ≥ 2 at Malnutrition Universal Screening Tool, and 95 showed dysphagia needing logopedic treatment. CONCLUSIONS: Our analysis shows that subjects admitted for in-patient pulmonary rehabilitation after severe COVID-19 represent an extraordinarily multifaceted and clinically complex patient population who need customized, comprehensive rehabilitation programs carried out by teams with different professional skills. The need for step-down facilities, such as sub-intensive rehabilitation units, is also highlighted.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".