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
Background: Dermatology lexicon is rich with descriptive terminology. However, for a variety of reasons, it also includes a number of misnomers. ObjectiveTo review the more commonly encountered and critically appraised misnomers in dermatology. Methods: A search of MEDLINE (1966 – 2004), eMedicine dermatology text and electronic versions of two standard dermatology texts, Fitzpatrick's Dermatology in General Medicine and Dermatology, was performed using the permutations of the terms: dermatology, skin, cutaneous, and misnomer. Results: Greater than 40 misnomers were identified. Conclusions: Misnomers in dermatology stem largely from lack of appreciation of underlying etiology or histopathological features of certain skin conditions, imprecise historical observations and erroneous eponymous credit. Historical, clinical, or histopathological explanations are used to clarify the nature of the misnomers, and in some cases suggestions for improved terminology are provided.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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