Lymphocytopenia; induced by vinorelbine, doxorubicin and cisplatin in human cancer patients
Bibliographic record
Abstract
Cancer chemotherapy exerts deleterious effects in patients, causing structural and physiological changes to their vital organs. These drugs are capable of destroying bone marrow cells and may reduce lymphocyte count. This study was aimed to investigate the frequency of lymphocytopenia in cancer patients taking vinorelbine and its combination as part of cancer chemotherapy. A total 60 adult cancer patients were selected and divided into two groups; Group-1 patients were either on Vinorelbine alone treatment protocol, while group 2 patients were on either Vinorelbine/Cisplatin or Vinorelbine/Doxorubicin treatment protocol. The mean ± SEM lymphocyte counts (× 10 ^{3} ) per uL, pre and post chemotherapy were noted. The outcomes demonstrated no statistically important difference in the patients who were either on vinorelbine alone, vinorelbine plus cisplatin or vinorelbine plus doxorubicin combinations. On comparison of the lymphocytopenia over time for Group-I & II (P-values 0.064, 0.23), and at every week (P-value -0.063, 0.427), we observed the non significant statistical differences. However, comparison of mean values before with that of at week-1,2 and 3 showed significant (P-value ^{3} 0.003, 0.003 and 0.055), and at week-4 no significant difference (P-value ^{3} 0.727). Thus, the overall lymphocytopenic syndrome in both of the chemotherapy protocols allows the clinical oncologists and consultant physicians to select either of the chemotherapy protocol. Vinorelbine may be a choice of cancer chemotherapy, as they do not compromise immunity in cancer patients. Hence; therapeutic efficacy should constitute the intervening consideration in treating a particular neoplasm.
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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 | 0.000 | 0.000 |
| 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".