On the detection of peripheral cyanosis in individuals with distinct levels of cutaneous pigmentation
Bibliographic record
Abstract
Peripheral cyanosis, the purple or blue coloration of hands and feet, can represent the initial signs of life-threatening medical conditions such as heart failure due to coronary occlusion. This makes its effective detection relevant for the timely screening of such conditions. In order to reduce the probability of false negatives during the assessment of peripheral cyanosis, one needs to consider that the manifestation of its characteristic chromatic attributes can be affected by a number of physiological factors, notably cutaneous pigmentation. The extent to which cutaneous pigmentation can impair this assessment has not been experimentally investigated to date, however. Although the detection of peripheral cyanosis in darkly-pigmented individuals has been deemed to be impractical, data to support or refute this assertion are lacking in the literature. In this paper, we address these issues through controlled in silico experiments that allow us to predictively reproduce appearance changes triggered by peripheral cyanosis (at different severity stages) on individuals with distinct levels of cutaneous pigmentation. Our findings indicate that the degree of detection difficulty posed by cutaneous pigmentation can be considerably mitigated by selecting the appropriate skin site to perform the observations.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".