Not Waiting to Progress; How the COVID-19 Pandemic Nudged Neoadjuvant Therapy for Stage III Locally Advanced Melanoma Patients
Bibliographic record
Abstract
Background: Early-phase neoadjuvant trials have demonstrated promising results in the utility of upfront immunotherapy in locally advanced stage III melanoma and unresected nodal disease. Secondary to these results and the COVID-19 pandemic, this patient population, traditionally managed through surgical resection and adjuvant immunotherapy, received a novel treatment strategy of neoadjuvant therapy (NAT). Methods: Patients with node-positive disease, who faced surgical delays secondary to COVID-19, were treated with NAT, followed by surgery. Demographic, tumour, treatment and response data were collected through a retrospective chart review. Biopsy specimens were analysed prior to the initiation of NAT, and therapy response was analysed following surgical resection. NAT tolerability was recorded. Results: Six patients were included in this case series; four were treated with nivolumab alone, one with ipilimumab and nivolumab and one with dabrafenib and trametinib. Twenty-two incidents of adverse events were reported, with the majority (90.9%) being classified as grade one or two. All patients underwent surgical resection: three out of six patients following two NAT cycles, two following three cycles and one following six cycles. Surgically resected samples were histopathologically evaluated for the presence of disease. Five out of six patients (83%) had ≤1 positive lymph node. One patient showed extracapsular extension. Four patients demonstrated complete pathological response; two had persisting viable tumour cells. Conclusions: In this case series, we outlined how in response to surgical delays secondary to the COVID-19 pandemic, NAT was successfully applied to achieve promising treatment response in patients with locally advanced stage III melanoma.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".