The prognostic signficance of pretreatment leukocytosis in patients with anal cancer treated with radical chemoradiotherapy or radiotherapy.
Bibliographic record
Abstract
656 Background: There are emerging data showing prognostic significance of pre-treatment leukocytosis (elevated white blood cell count) in cervical cancer patients. However the prognostic impact of leukocytosis in anal cancer patients has not been previously reported. The purpose of this study was to determine the association of pre-treatment leukocytosis on outcome in patients with anal cancer treated with radical chemoradiotherapy (CRT) or radiotherapy (RT). Methods: 126 patients with anal cancer, treated with radical CRT (91.3%) or RT (8.7%) from 2 major Canadian cancer centers (University of Calgary, n=65 and University of Alberta, n=61), between 2000 and 2008 were evaluated. Demographic, clinical, hematologic and treatment factors were retrieved from retrospective review of the patients’ records. The association of clinical factors and hematologic status with overall survival (OS) and disease-free survival (DFS) was analyzed using Cox proportional hazards regression models. Results: Median follow-up was 24 months. Median tumor size was 4 cm. Mean age was 59 years and M:F was 29:97. Pre-treatment leukocytosis (WBC count greater than 10^9/L) was identified in 16% (20/126) of patients. After adjusting for gender, tumor size and stage in a multivariate analysis, leukocytosis remained significantly associated with worse 2-year OS [HR 2.9 (95% CI 1.1-7.9), p=0.036] and worse DFS [HR 2.2 (95% CI1.1-4.8), p=.045]. The patient group with both pre-treatment hemoglobin (Hgb) less than 125 g/L (lowest quartile) and leukocytosis had very poor outcomes, 2-year OS 61% versus 89% for patients without these factors; more than doubling the hazard for DFS [HR2.7 (95% CI 1.1-6.8), p=0.033] and for OS [4.5 (95% CI 1.5-13.2), p=.006]. Conclusions: Pre-treatment leukocytosis is associated with worse OS and DFS in patients with anal cancer treated with radical CRT or RT. Patients with both low Hgb and leukocytosis had very poor outcomes. These hematologic parameters represent potential biomarkers for prognosis and treatment response, and warrant further investigation to uncover the underlying biologic mechanisms and therapeutic strategies in this patient group.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.001 | 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".