What is the Minimal Clinically Important Difference in the OneMinute Sit-to-Stand Test During Remote Interventions?
Bibliographic record
Abstract
BackgroundRehabilitation of patients recovering from critical illness has been a focus of clinical research, however heterogeneity exists in the measurement of physical recovery.Wearable technology offers a simple, unobtrusive method of continuous activity monitoring.However, studies have reported issues with placement of wrist and ankle devices in critically ill patients. AimTo evaluate the feasibility of a thigh-worn accelerometer to measure the physical activity of patients recovering from critical illness. MethodsA prospective observational feasibility study was conducted within a 33-bedded critical care unit over nine weeks.Thigh-worn activPAL TM accelerometers were applied to patients for the duration of their critical care admission. ResultsA total of 12 participants were recruited.Median (IQR) device wear time was 268.35 (299.15)hours or 99.58% of their critical care admission.A priori feasibility success criteria of three days of wear time for more than ten hours were achieved in every participant.Device removal primarily occurred for recharging. ConclusionThe practical application of a thigh-worn accelerometer may be feasible in this population for the duration of admission.As this was a small, single centre study additional research is necessary to further inform and determine the feasibility of this device.
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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 | 0.013 | 0.040 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".