Bibliographic record
Abstract
OBJECTIVE: To assess the diagnostic accuracy and clinical utility of dermoscopy for melanoma detection in family practice. QUALITY OF EVIDENCE: Ovid MEDLINE (1946 to June 2011), EMBASE, PubMed, and Cochrane databases were searched using the following terms: dermoscopy, dermatoscopy, epiluminescence microscopy, family practice, general practice, primary health care, melanoma, skin neoplasms, and pigmented nevus. To be included, studies had to be primary research articles with family physicians as the subjects and dermoscopy training and use as the intervention. Four papers met all inclusion criteria and provided level I evidence according to the Canadian Task Force on Preventive Health Care definition. MAIN MESSAGE: Among family physicians, dermoscopy has higher sensitivity for melanoma detection than naked-eye examination with generally no decrease in specificity. Dermoscopy also helps to increase family physicians' confidence in their preliminary diagnosis of lesions. When using dermoscopy, compared with naked-eye examination, there is a higher likelihood that a lesion assessed as being malignant is in fact malignant and that a lesion assessed as being benign is in fact benign. CONCLUSION: Dermoscopy has been shown to be a useful and fairly inexpensive tool for melanoma detection in family practice. This technique can increase family physicians' confidence in their referral accuracy to dermatologists and can assist in decreasing unnecessary biopsies. Dermoscopy might be especially useful in examining patients at high risk of melanoma, as the current Canadian clinical practice guideline recommends yearly screening in these individuals.
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 | 0.005 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".