Factors Influencing Outcomes and Survival in Anal Cancer
Bibliographic record
Abstract
Background: We aim to ascertain prognostic factors in the current management of anal cancer within this study. Methods: We reviewed the management and outcomes of anal cancer cases over a seven-year period, inclusive (2016–2023). The primary objectives were to assess the demographic characteristics, clinical presentation, and outcomes of all anal cancer patients within our institution. Kaplan–Meier survival analysis was used to estimate survival differences between cohorts, with statistical significance determined using log-rank testing. Cox proportional hazards regression was utilised to identify prognostic factors. Cox regression hazard ratios were reported along with confidence intervals and p-values. Results: The median follow-up time for the study was 29.8 months. Seventy-five patients with anal cancer were included in this study, with 88% (66/75) being squamous cell carcinoma (SCC) and the majority having regional disease (82.7% (62/75)). The median age at diagnosis was 63.4 years (36–94). There was a female preponderance (57.3% (43/75)). In total, 84% (63/75) underwent definitive chemoradiation (dCRT), with 7/63 (11.1%) requiring a salvage abdomino-perineal resection (APR) for residual or recurrent disease. Adverse prognostic indicators include those with T4 disease hazard ratio = 3.81, (95% CI 1.13–12.83, * p = 0.04), poorly differentiated tumour disease HR = 3.37, (95% CI 1.13–10.02, * p = 0.04), having N2 nodal status HR = 5.03, (95% CI 1.11–22.8, * p = 0.04), and having metastatic disease at diagnosis HR = 5.8, (95% CI 1.28–26.42, * p = 0.02). Conclusion: Presenting characteristics including stage, nodal, and differentiation status remain key prognostic indicators in those diagnosed with anal malignancy.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".