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
An overview of the long-established methods of diagnosing onychomycosis (potassium hydroxide testing, fungal culture, and histopathological examination) is provided followed by an outline of other diagnostic methods currently in use or under development. These methods generally use one of two diagnostic techniques: visual identification of infection (fungal elements or onychomycosis signs) or organism identification (typing of fungal genus/species). Visual diagnosis (dermoscopy, optical coherence tomography, confocal microscopy, UV fluorescence excitation) provides clinical evidence of infection, but may be limited by lack of organism information when treatment decisions are needed. The organism identification methods (lateral flow techniques, polymerase chain reaction, MALDI-TOF mass spectroscopy and Raman spectroscopy) seek to provide faster and more reliable identification than standard fungal culture methods. Additionally, artificial intelligence methods are being applied to assist with visual identification, with good success. Despite being considered the 'gold standard' for diagnosis, clinicians are generally well aware that the established methods have many limitations for diagnosis. The new techniques seek to augment established methods, but also have advantages and disadvantages relative to their diagnostic use. It remains to be seen which of the newer methods will become more widely used for diagnosis of onychomycosis. Clinicians need to be aware of the limitations of diagnostic utility calculations as well, and look beyond the numbers to assess which techniques will provide the best options for patient assessment and management.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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