Impact of perioperative blood transfusion on survival among women with breast cancer.
Bibliographic record
Abstract
e21591 Background: Blood transfusion has immune-modulating effect which may affect outcome in women with breast cancer including cancer recurrence and survival. Methods: PubMed, Embase and Cochrane libraries were searched by utilizing different combinations of keywords: blood transfusion, survival, mortality and breast cancer. Inclusion criteria were: (1) English articles and (2) studies with an adult population with the diagnosis of breast cancer undergoing resection which may or may not have received blood transfusion. Four hundred and seventy studies were reviewed, 13 studies meeting inclusion criteria were pooled for Meta-analysis. Results: Twenty five datasets from 13 studies with total N = 7384 subjects (transfused 2264, not transfused 5120) were pooled. Survival rates recorded at a specified time frame, this range from 4-12 years. Major confounding factors effecting survival in this population may include age, presence of other chronic illnesses, spread of breast cancer to lymph nodes, stage of cancer and extent of cancer treatment. Type, group and number of blood units transfused may also play a role. Survival rates among not transfused group range from 34% to 79% while in transfused group range from 41% to 78%. Significant heterogeneity was detected among studies; therefore random effect model was used. Comparing women without blood transfusion, women with transfusion have Odd ratio of overall survival 0.79 (0.72 to 0.86, I2= 47.3, P = 0.001). we also performed sensitivity and subgroup analysis. Conclusions: Perioperative blood transfusion appears to adversely impact survival among women with 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 | 0.006 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.012 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".