Effectiveness of a multidisciplinary programme to improve functional outcomes of patients following severe COVID-19 infection in Malaysia: a retrospective study
Bibliographic record
Abstract
Background/Aims Evidence-based, specialised rehabilitation interventions are key to improving functional outcomes for patients with complications caused by severe COVID-19 infection, who often have complex needs and a wide range of functional impairments. The aims of this study were to determine the effectiveness of a structured inpatient, personalised, interdisciplinary rehabilitation programme, namely the COVID-19 Rehabilitation Inpatient Specialised Services, and to identify clinical predictors of rehabilitation effectiveness in patients after contracting COVID-19. Methods This retrospective study involved 154 patients who underwent rehabilitation under the COVID-19 Rehabilitation Inpatient Specialised Services programme at a single centre between 1 July and 31 October 2021. The modified Barthel Index, Post-COVID-19 Functional Scale, modified Medical Research Council Dyspnoea Scale scores, and actual effectiveness derived from the modified Barthel Index scores were used to measure outcomes. Results The mean age of patients was 49.8 ± 14.3 years. Overall 48.1% (n=74) had required intubation, 70.1% (n=108) had been critically ill, and 21.4% (n=33) remained dependent on oxygen therapy beyond discharge. There was a statistically significant improvement in mean modified Barthel Index scores (45.2 vs 66.3, P<0.001), median post-COVID-19 Functional Scale score (4 vs 3, P<0.001) and median modified Medical Research Council scores (4 vs 3, P<0.001) following the rehabilitation intervention. Acute kidney injury, oxygen therapy dependency, neurological complications and initial modified Barthel Index scores were significant predictors of rehabilitation effectiveness (adjusted R2=0.23, P<0.001). Conclusions The COVID-19 Rehabilitation Inpatient Specialised Services programme was effective in improving functional outcomes of hospitalised patients with severe to critical COVID-19 infection. By identifying factors that predict rehabilitation effectiveness, allied healthcare professionals can administer more focused rehabilitation efforts tailored to the specific needs of patients, thereby enabling them to achieve their maximum potential functional outcomes.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".