A quality assurance review of penile cancer diagnostic delays and stage at presentation during the COVID-19 pandemic
Bibliographic record
Abstract
INTRODUCTION: Penile carcinomas represent a rare malignancy associated with significant psychosocial impacts that deter afflicted individuals from seeking medical attention, thus, worsening prognosis. Following the dramatic shift in healthcare delivery to virtual platforms, it is suspected that prevalent psychosocial impacts have been further compounded by the COVID-19 pandemic, resulting in several late-stage presentations and engendering poorer outcomes. METHODS: A retrospective chart review of surgically managed cases of penile cancer was conducted from January 2020 to June 2022 to identify patients that may have been unduly impacted by the COVID-19 pandemic. Included cases were analyzed in quantifying diagnostic and treatment delays, along with patient outcomes. Relevant epidemiological and pathological markers were also examined. RESULTS: Ten patients met the inclusion criteria. Average time delay from first complaint of a penile lesion to surgical management was 75 days, with 60% of patients experiencing a time delay of two months or more. The average delay from first complaint to diagnosis was 62 days in 2020 and 18 days in 2021. Advanced-stage disease was present in six (60%) individuals at presentation, while four (40%) patients perished during the study period. CONCLUSIONS: In cases of concern for penile malignancy, virtual care cannot replace the necessity of physical exams in preventing diagnostic and treatment delays. The present study further highlights the necessity of initial physical examination of penile abnormalities in preventing fatal outcomes for those afflicted. Such consideration warrants urgent examination of referred males with genital abnormalities to prevent further exacerbation of delays.
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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 | 0.043 | 0.124 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.017 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".