The Silent Spread: A Systematic Review of Delayed Melanoma Diagnosis and Disease Progression During the COVID‐19 Pandemic
Bibliographic record
Abstract
ABSTRACT Background The COVID‐19 pandemic has significantly disrupted global healthcare systems. Melanoma, a highly aggressive skin cancer whose prognosis is closely tied to early detection, has been particularly impacted. Lockdowns, changes to healthcare resource reallocation and patient hesitancy in seeking care have all contributed to delays in melanoma diagnoses and treatment modifications. Understanding the extent of this disruption is crucial to inform future healthcare planning during global crises. Objectives To evaluate the impact of the COVID‐19 pandemic on melanoma incidence, diagnostic delays and disease severity by comparing findings from pre‐pandemic and pandemic periods through a systematic review of existing literature. Methods A systematic search was conducted across PubMed, Scopus, Web of Science and Cochrane Library in September 2024, adhering to PRISMA guidelines. Eligible studies included clinical trials, observational studies, reviews and case series that reported melanoma incidence, diagnostic delays or changes in treatment between pre‐COVID‐19 (2015−2019) and COVID‐19/post‐COVID‐19 (2019−2024) periods. Data extraction focused on incidence rates, Breslow thickness, ulceration, treatment changes and patient outcomes. Study quality was assessed using the Newcastle−Ottawa Scale and Joanna Briggs Institute tools. Results Out of 503 studies screened, 55 met the inclusion criteria. Most studies reported a decline in diagnosed melanoma cases during the pandemic, accompanied by an increase in Breslow thickness and ulceration rates, indicating delayed presentations and more advanced disease. Significant disruptions in treatment pathways, including reduced surgical excisions, were frequently noted. Conclusions The COVID‐19 pandemic contributed to delayed melanoma diagnoses and a shift towards more advanced disease at presentation. These findings highlight the critical need for resilient cancer care systems to maintain timely diagnostic and treatment services during future public health emergencies.
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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 | 0.007 | 0.278 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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) | 0.000 | 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".