Expression of ZAP 70 in Chronic Lymphocytic Leukemia (CLL) and its Correlation with Clinical Stages
Bibliographic record
Abstract
Objective: To ascertain the levels of ZAP-70 expression within the context of Chronic Lymphocytic Leukemia (CLL) patients and to subsequently analyze the potential correlations between ZAP-70 expression and the clinical staging of the disease. Study Design & Setting: Cross-sectional, descriptive study. Department of Hematology, Armed Forces Institute of Pathology (AFIP), Rawalpindi from July 2021 to December 2021. Methodology: We included 73 CLL patients of various ages and both genders. and patients having lymphoproliferative disorders other than CLL were excluded. Sysmex XN-3000 was used to do complete blood counts. Diagnosis of CLL was confirmed immunophenotypically by flow cytometry. Samples were processed by standard methods for ZAP70 analysis. Descriptive statistics were expressed in terms of mean ± standard deviation (SD). A Chi-square test was conducted, with a significance level of p-value =0.05 being considered significant. Results: Among 73 patients, 24 were females (32.9%) and 49 were males (67.1%). Females had a mean age of 69.00±9.47 years, whereas males had a mean age of 65.73±11.12 years. ZAP-70 expression was positive in 8 (11%) and negative in 65 (89.0%) cases. The expression of ZAP-70 in CLL and its correlation with Binet Stages was significant (p=0.002). ZAP- 70 expression in males and females showed no significant difference (p 0.059). ZAP-70 analysis showed no significant difference in patients with age more than 60 years from = 60 years of age (p= 0.275) Conclusion: ZAP-70 gene expression should be used in routine laboratory settings and can be correlated with the clinical stages of CLL
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".