COVID-19 Early Detection Tool for Elder Abuse during Epidemics, Digital Analysis of Color Tone on the Surface of the Skin in Elderly People
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it