Physiotherapy input and outcomes in patients hospitalised with COVID-19: A retrospective chart review
Bibliographic record
Abstract
Objectives The primary aim of this study was to profile the acute physiotherapy service provided to patients admitted to hospital with COVID-19. Design A retrospective observational chart review was completed on all patients admitted to an acute hospital with a confirmed diagnosis of COVID-19 between March and May 2020. Participants All patients admitted to hospital with a COVID-19 diagnosis receiving ICU and/or ward-based care were included in this study. Main outcome measures Baseline information (including demographic information and mobility status), physiotherapy treatment information, time to achieve functional mobility goals and discharge information (including length of stay, discharge destination and mobility status on discharge) was collected for each patient. Results A retrospective chart review of 171 charts was performed. Patients admitted for ward-based care (n = 130) were referred to physiotherapy on average 3 days post hospital admission (SD 4.45) with 72% of patients documented as ‘off pre-morbid mobility’ on initial physiotherapy assessment. Of the 100 patients discharged from ward-based care, 86 (87%) of patients returned to baseline mobility status. All patients were referred for physiotherapy during their critical care stay (n = 41, range: 0–49 sessions). Of the 35 patients who survived an ICU stay, 26 (71%) patients had returned to baseline pre-morbidity mobility status on discharge home while 5 (14%) patients required increased aids/assistance with mobility when compared to baseline function. A further 4 patients (11%) were still receiving inpatient rehabilitation at the time of chart review. Factors such as older age and longer hospital length of stay were significant predictors of likelihood of referral to physiotherapy. Conclusions This chart review found a significant number of patients hospitalised with COVID-19 required physiotherapy input, particularly older patients and those with a longer hospital length of stay.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| 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".