Protocol for a systematic review of reviews on training primary care providers in dermoscopy to detect skin cancers
Bibliographic record
Abstract
INTRODUCTION: Globally, incidence, prevalence and mortality rates of skin cancers are escalating. Earlier detection by well-trained primary care providers in techniques such as dermoscopy could reduce unnecessary referrals and improve longer term outcomes. A review of reviews is planned to compare and contrast the conduct, quality, findings and conclusions of multiple systematic and scoping reviews addressing the effectiveness of training primary care providers in dermoscopy, which will provide a critique and synthesis of the current body of review evidence. METHODS AND ANALYSIS: Four databases (Cochrane, CINAHL, EMBASE and MEDLINE Complete) will be comprehensively searched from database inception to identify published, peer-reviewed English-language articles describing scoping and systematic reviews of the effectiveness of training primary care providers in the use of dermoscopy to detect skin cancers. Two researchers will independently conduct the searches and screen the results for potentially eligible studies using 'Research Screener' (a semi-automated machine learning tool). Backwards and forwards citation tracing will be conducted to supplement the search. A narrative summary of included reviews will be conducted. Study characteristics, for example, population; type of educational programme, including content, delivery method, duration and assessment; and outcomes for dermoscopy will be extracted into a standardised table. Data extraction will be checked by the second reviewer. Methodological quality will be evaluated by two reviewers independently using the Critical Appraisal Tool for Health Promotion and Prevention Reviews. Results of the assessments will be considered by the two reviewers and any discrepancies will be resolved by team consensus. ETHICS AND DISSEMINATION: Ethics approval is not required to conduct the planned systematic review of peer-reviewed, published articles because the research does not involve human participants. Findings will be published in a peer-reviewed journal, presented at leading public health, cancer and primary care conferences, and disseminated via website postings and social media channels. PROSPERO REGISTRATION NUMBER: CRD42023396276.
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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 | 0.138 | 0.177 |
| Meta-epidemiology (narrow) | 0.009 | 0.010 |
| Meta-epidemiology (broad) | 0.024 | 0.022 |
| Bibliometrics | 0.019 | 0.019 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.012 | 0.016 |
| Open science | 0.009 | 0.009 |
| Research integrity | 0.013 | 0.015 |
| Insufficient payload (model declined to judge) | 0.134 | 0.026 |
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".