A Validated Grading Scale for Forehead Lines
Bibliographic record
Abstract
BACKGROUND: As with other facial wrinkles, the gradual loss and disorganization of collagen fibers and elastin, connective tissues that provide underlying support for skin, cause horizontal forehead rhytides in large part. OBJECTIVES: To develop the Forehead Lines Grading Scale for objective quantification of horizontal forehead rhytides at rest and with expression and to establish the reliability of this photonumeric scale for clinical research and practice. MATERIALS AND METHODS: The Forehead Lines Grading Scale is a 5-point photonumeric rating scale that was developed to objectively quantify resting (static) and hyperkinetic (dynamic) forehead lines. Inter- and intrarater variability was assessed by computing intraclass correlation coefficients. RESULTS: The agreement between the experts was highly significant. Bubble plots (bivariate scatter plots) demonstrated linearity in judgment by the experts. CONCLUSION: The 5-point photonumeric scale generated spans the severity of forehead lines for which patients commonly seek correction. The scale is well stratified for consistent rating.
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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".