USING TECHNOLOGIES TO IMPROVE HEALTHCARE AND QUALITY OF LIFE IN THE VULNERABLE ELDERLY
Bibliographic record
Abstract
Aging is often associated to higher risk for many negative health related events. For example, it is well known that falls increase significantly with age and their consequences are often dramatic. Similarly, the changes observed in seniors, be them associated with normal aging or pathological processes, often generate important mobility impairments that can greatly affect quality of life. Finally, behavioral and psychological symptoms of dementia (BPSD) and chronic pain are serious health problems in aging populations that can negatively affect the wellbeing of patients and the work environment of the healthcare team. Early detection of falls and limitations in community mobility, as well as identification and management of BPSD and chronic pain in elderly individuals who have difficulty communicating because of dementia remain a challenge for healthcare providers. The current symposium will address these important issues and will provide concrete examples on how technologies such as intelligent videomonitoring, mobile applications on smartphones and wearables sensors can be leveraged to provide outcome measures that can be used to identify elderly individuals who fall in their environment, have mobility limitations, exhibit disturbing BPSD or who suffer from chronic pain. More than simply detecting falls, mobility limitations, BPSD and pain, the proposed technologies offer healthcare professionals the possibility of assessing the underlying/contributing factors related to these negative health related events, in order to propose an approach of care that has a beneficial impact for the patient and can improve his quality of life.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".