COVID-19 Early Detection Tool for Elder Abuse during Epidemics, Digital Analysis of Color Tone on the Surface of the Skin in Elderly People
Bibliographic record
Abstract
The purpose of this study was to attempt a digital analysis of body color tone of elderly subjects, thus demonstrating that nurses and caregivers can easily and reliably record changes in body color tone. This cross-sectional study took place between April 1, 2017 and March 31, 2019. A workshop was set up where observers received explanations from researchers on how to use color charts and recording forms. Measurement instruments (digital cameras) were also standardized in this effort. While the elderly subjects targeted by this study suffered from dementia, they were able to converse and understood the purpose of the study, and the study was conducted with their and their families’consent. In addition, after receiving approval from a research ethics examination from an affiliated university, the target facility gaining this consent was subjected to an ethical review, after which we implemented the study in accordance with ethical guidelines for medical research on humans. Consent was obtained from 30 subjects (20 female (66.7%), 8 male (26.7%) and 2 for which the gender was unknown; average age: 87.8 years (minimum 80 years, maximum 100 years)). We were able to perform digital image analysis of the lesion site and unaffected parts, and present numerical values. Evaluations by observers were significantly different depending on the individual, and subjectivity greatly influenced comparisons with the color chart based on visual evaluations. It was confirmed that numerical evaluation of images taken in hospitals and nursing homes could also be performed using general-purpose software.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".