INTELLIGENT HOME RISK-BASED MONITORING SOLUTIONS FOR POST ARTHROPLASTY SURVEILLANCE
Bibliographic record
Abstract
A key challenge for healthcare delivery in OECD countries is the projected significant increases in populations over the age of 65 years. Australia for example will experience an increase of 16.4% by 2015 while Canada will experience an increase of 16%, UK an increase of 17.9% and US an increase of 14.3% during the same time period (Australian Bureau of Statistics, 2010). Increases of such magnitude will have significant and far reaching implications for healthcare delivery, labour force participation, housing and demand for skilled labour (Australian Bureau of Statistics, 2010). Given the impending economic impact of providing healthcare services to this projected increase of seniors, examination of technology solutions that serve to provide effective and efficient healthcare delivery during the peri and postoperative care process are highly desired and help those desiring to age in place. Recent studies have demonstrated rapid growth in the number of seniors using computers in the US and other developed countries and is projected to increase further (Jimison et al., 2006). This technology adoption leads to further growth in the potential for health monitoring technologies (Clifford and Clifton, 2012) with the key aim being the maintenance of a seniors' autonomy through understanding how he or she can manage his or her individual health problem and what necessary actions should be taken and when (Ludwig et al., 2012). Projections by the Congressional Budget Office for Social Security, Medicare, and Medicaid transfers as a percentage of GDP show the share of output spent on seniors' care programs in US rising from 7.6% in 2000 to 13.9% in 2030 to 21.1% in 2075 (Zhang et al., 2009, Falls, 2008). Despite the increased number of home monitoring technologies in age care contexts, there are several challenges that have to be met before integrating such services into the practice, as a real-life application (Ludwig et al., 2012). As the incidence of arthroplasty surgery is projected to increase over six fold between 2010 and 2030 in the US (Kurtz, Ong, Lau, Mowat, & Halpern, 2007), the post arthroplasty period represents a challenging environment for the adoption of new monitoring technologies to optimize the rehabilitative and recovery process. This study develops a framework for post-arthroplasty monitoring through the application of the intelligence continuum (Wickramasinghe and Schaffer, 2006) to the post-arthroplasty care process including an analysis of the risks and complications. The benefits, barriers and critical elements of designing the theory based framework for home-monitoring technologies provides the structural framework for clinical application of the monitoring modalities. The entire arthroplasty process is included in order to provide appropriate management governance (figure 1) with the following metrics:
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".