Impact of the <scp>COVID</scp> pandemic on melanoma thickness and ulceration: a meta‐analysis
Bibliographic record
Abstract
The global healthcare sector faced immense challenges due to the COVID-19 pandemic. Oncologists noted reduced cancer screening, which impacted melanoma diagnosis and treatment, leading to concerns about delayed care and poorer outcomes. This review analyzes how the pandemic influenced melanoma ulceration risk and Breslow thickness index through a meta-analysis of published studies. Following PRISMA guidelines, we conducted a systematic review of literature from January 2021 to December 2022 on cutaneous melanoma before and during the COVID-19 pandemic. Upon screening 1854 manuscripts, the review led to 13 studies meeting inclusion standards. The quality assessment followed MINORS and Newcastle-Ottawa Scale criteria. Regarding ulceration, post-COVID ulceration surpassed pre-COVID levels significantly, with a risk ratio of 1.31 and an estimated odds ratio of 1.41, indicating a 44% rise post-COVID. As for Breslow thickness, studies show a rising trend in the Breslow index post-COVID, but less significantly, with an effect size of 0.08 regarding the meta-analysis model (P = 0.02) with a pre-COVID mean Breslow of 1.56 mm and post-COVID of 1.84 mm. This meta-analysis concluded that post-COVID ulceration rates significantly surpassed pre-COVID levels. Considering that ulcerated melanomas usually undergo sentinel lymph node biopsy and are more likely to benefit from adjuvant therapies, this indicates important implications, as many patients might have missed the opportunity to start therapy appropriately, regardless of their Breslow thickness status.
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How this classification was reachedexpand
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 | 0.003 |
| 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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".