REAL-TIME LOCATION SYSTEMS FACILITATE INDEPENDENCE IN LONG-TERM AND SPECIALIZED CARE SETTINGS
Bibliographic record
Abstract
In this symposium, speakers will discuss clinical and research initiatives utilizing real-time location systems (RTLS) to promote functional independence of vulnerable adults in home and clinical environments. The intended use of RTLS to inform clinical decision-making will be described. Session one will focus on RTLS to monitor older residents’ movements in an assisted living environment; data reduction and analysis techniques that map resident ambulation patterns around the facility and reveal intra-individual pattern changes in order to predict falls in this vulnerable population will be presented. Session two presents a unique integration of RTLS with bed monitors to support a nurse-driven clinical initiative promoting early and progressive ambulation in hospitalized Veterans. The validity, sensitivity and specificity of system-generated reports of patient time in motion, out of bed but sedentary, and at bed rest and the challenges of differentiating various modes of Veteran movement will be discussed. In the final session, ongoing work on model “Smart Home” integrated systems to support Veterans with traumatic brain injury in various environments will be presented. The session will describe a system that prompts a patient to perform specific functional tasks, the data integration strategies used to detect the behavior sequences in the task, and planned enhancements to the system that will reengage a Veteran to resume interrupted behavior sequences at the point in the sequence where the requisite behaviors have been omitted.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.003 |
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".