The Clinical Importance of Measurement of Hematological Indices in the Breast Cancer Survivals: A Comparison Between Premenopausal and Postmenopausal Women
Bibliographic record
Abstract
BACKGROUND: Determination of the hematological indices is a useful prognostic laboratory investigation in the cancer research. The neutrophil to lymphocyte ratio (LNR), red cell distribution width (RDW) and the platelet distribution width (PDW) are useful markers for the prediction and the prognosis of breast cancer. The aims of this study were to assess the hematological indices in breast cancer women survivals and to show if there were significant differences in these indices between pre- and postmenopausal women. METHODS: This observational study was carried out in the Nanakali Hospital in Erbil, Kurdistan region, Iraq. A total number of 120 women with breast cancer under different modalities of management were enrolled in this study. The patients were grouped into premenopausal (group I, n = 30) and postmenopausal (group II, n = 90) women and the hematological indices of all patients were determined. RESULTS: Significant low hemoglobin levels and red cell counts were observed among group II compared with group I patients. Group II women had significant high values of RDW and mean platelet volume (MPV) (16.68 ± 2.51 and 9.980 ± 1.271) compared with group I (15.12 ± 2.27 and 9.535 ± 1.082). There were insignificant differences between group I and group II regarding the values of the PWD, plateletcrit (PCT), NLR and platelet to lymphocyte ratio (PLR). CONCLUSIONS: We conclude that the low hemoglobin levels, and the high RDW and PDW are significantly existing in postmenopausal compared with premenopausal survival women, indicating that there are specific hematological indices associated with the postmenopausal survival of the breast cancer.
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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 | 0.001 | 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".