The Use of Fractal Analysis and Photometry to Estimate the Accuracy of Bulbar Redness Grading Scales
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
PURPOSE: To use physical attributes of redness to determine the accuracy of four bulbar redness grading scales, and to cross-calibrate the scales based on these physical measures. METHODS: Two image-processing metrics, fractal dimension (D) and percentage of pixel coverage (% PC), as well as photometric chromaticity were selected as physical measures, to describe and compare grades of bulbar redness among the McMonnies/Chapman-Davies scale, the Efron Scale, the Institute for Eye Research scale, and a validated scale developed at the Centre for Contact Lens Research. Two sets of images were prepared by using image processing: The first included multiple segments covering the largest possible region of interest (ROI) within the bulbar conjunctiva in the original images; the second contained modified scale images that were matched in size and resolution across scales, and a single, equally-sized ROI. To measure photometric chromaticity, the original scale images were displayed on a computer monitor, and multiple conjunctival segments were analyzed. Pearson correlation coefficients between each set of image metrics and the reference image grades were calculated to determine the accuracy of the scales. RESULTS: Correlations were high between reference image grades and all sets of objective metrics (all Pearson's r >or= 0.88, P <or= 0.05); each physical attribute pointed to a different scale as being most accurate. Independent of the physical attribute used, there were wide discrepancies between scale grades, with almost no overlap when cross-calibrating and comparing the scales. CONCLUSIONS: Despite the generally strong linear associations between the physical characteristics of reference images in each scale, the scales themselves are not inherently accurate and are too different to allow for cross-calibration.
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 | 0.001 | 0.003 |
| 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 | 0.006 |
| 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