Bibliographic record
Abstract
Early mobilization in the intensive care unit (ICU) is an integral part of physiotherapy-led rehabilitation, however it remains difficult to objectively and consistently monitor patient mobility given that current practice is limited by intermittent clinical assessment and electronic health record (EHR)-based documentation. Wearable sensors are an objective and scalable approach to mobility assessment in the critically ill. To assess the validity; efficacy and clinical usability of accelerometry based wearable sensors to monitor mobility profiles in ICU patients from a physiotherapy standpoint. A prospective observational study design was used. Triaxial wearable accelerometers were applied on standardized locations on the body of adult ICU patients. Mobility features derived from the sensors such as activity count, transitions of posture and ambulation events were continuously recorded. This information was verified by direct observation, and contrasted with standard EHR documentation of mobility. We computed sensitivities, agreement measures and regression models to quantify the association between mobility levels as covariates in state-outcome pairings. Wearable sensors had high validity for detecting mobility activities important to ICU care, and superior sensitivity compared to EHR documentation. More independently objectively measured mobility was associated with a shorter ICU LOS and a higher discharge functional status. Wear time of the device and completeness of recording were (very) high, indicating feasibility in the general ICU. Wearable sensors delivers a valid, objective and clinically useful measure of mobility in ICU patients. Incorporated into daily physiotherapy practice they may assist in optimizing early mobilization interventions, provide decision-making structure and potential resource allocation to patient care in the critical care setting.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".