Comparison Between Chronic Lymphocytic Leukemia Patients in India and Canada
Bibliographic record
Abstract
Chronic lymphocytic leukemia (CLL) has long been recognized as the most common leukemia in North America, and is characterized by a highly variable clinical course. Some patients never require treatment whereas others require therapy at diagnosis. Prognosis can be predicted by stage at diagnosis, lymphocyte doubling time and biological markers, such as beta2-microglobulin, ZAP-70 or CD38. After a variable period of time, most patients will die from progressive CLL, or the complications of immunosuppression, such as infections and second malignancies. Data for CLL patients in Manitoba is available. However, in India, the interest in this disease is recent, as the population has aged and more cases of CLL are being diagnosed. It is unclear whether the clinical features of CLL are similar in India as in North America and no study has compared the two populations. In this study, we wish to compare the clinical features and outcome of CLL patients who attend the CLL clinic at CancerCare Manitoba against those that attend the All India Institute of Medicine in New Delhi, India. Clinical data will include age and sex of new patients, stage at diagnosis, lymphocyte doubling time and biological prognostic markers. In addition, time to treatment, type of treatment and survival will also be measured. Moreover, we will assess the cause of death in the two populations. These data will provide insight into differences in the characteristics of CLL in Canada and India
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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 | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".