A survey of office visits for actinic keratosis as reported by NAMCS, 1990-1999. National Ambulatory Medical Care Survey.
Bibliographic record
Abstract
Although actinic keratosis (AK) has been linked to the development of nonmelanoma skin cancer (NMSC), particularly squamous cell carcinoma (SCC), increased awareness regarding diagnosis and treatment may be an important component for reducing morbidity and even mortality from AK and NMSC. We used the National Ambulatory Medical Care Survey (NAMCS) data from 1990 to 1999 to evaluate the diagnosis and treatment of AKs among a wide variety of patients by physicians across the United States. To our knowledge, no widespread surveys of North American populations have been performed recently to determine the epidemiology of AK. AK was diagnosed in more than 47 million visits over the 10-year period surveyed and was found to occur in 14% of patients visiting dermatologists. The diagnosis of AK as determined by NAMCS does not reflect the true prevalence of AK because only patients seeking physician diagnosis were surveyed. This suggests that the actual number of patients in the United States with AK is much higher than 14%. Rates of AK diagnosis in the standard metropolitan statistical areas (SMSAs) and non-standard metropolitan statistical areas (non-SMSAs) of the West states are higher than in other states, but geographic location may not be a direct risk factor for the development of AKs. Procedures were undertaken at 70% of visits where AK was the primary diagnosis. Destruction of lesions was the most frequently performed procedure found in the survey considering only the 1993 and 1994 NAMCS data. Biopsy was the second most frequently performed procedure.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".