Application of the Perme Score to assess mobility in patients with COVID-19 in inpatient units
Bibliographic record
Abstract
Objective To evaluate the ability of the Perme Score to detect changes in the level of mobility of patients with COVID-19 outside the intensive care unit. Method A retrospective cohort study was conducted in inpatient units of a private hospital. Patients older than 18, diagnosed with COVID-19, who were discharged from the intensive care unit and remained in the inpatient units were included. The variables collected included demographic characterization data, length of hospital stay, respiratory support, Perme Score values at admission to the inpatient unit and at hospital discharge and the mobilization phases performed during physical therapy. Result A total of 69 patients were included, 80% male and with a mean age of 61.9 years (SD=12.5 years). The comparison of the Perme Score between the times of admission to the inpatient unit and at hospital discharge shows significant variation, with a mean increase of 7.3 points (95%CI:5.7-8.8; p <0.001), with estimated mean values of Perme Score at admission of 17.5 (15.8; 19.3) and hospital discharge of 24.8 (23.3; 26.3). There was no association between Perme Score values and length of hospital stay (measure of effect and 95%CI 0.929 (0.861; 1.002; p =0.058)). Conclusion The Perme Score proved effective for assessing mobility in patients diagnosed with COVID-19 with prolonged hospitalization outside the intensive care setting. In addition, we demonstrated by the value of the Perme Score that the level of mobility increases significantly from the time of admission to inpatient units until hospital discharge. There was no association between the Perme Score value and length of hospital 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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".