[Comparative Profiles Of Breast Cancers According To Tumor Type At The Gabriel Touré University Hospital, Bamako - Mali, Between 2018 And 2022].
Bibliographic record
Abstract
BACKGROUND: Few studies have been conducted on breast cancer despite its high burden in our context. Therefore, this study aimed to: (1) specify the sociodemographic and clinical characteristics of breast cancer; and (2) determine the factors associated with breast cancer survival at Gabriel Touré University Hospital (CHU). METHODS: , December 2022. Histologically confirmed cases of breast cancer were included and divided into three anatomoclinical groups (non-T4 tumor [NT4], locally advanced cancer [LAC] and inflammatory breast cancer [IBC]). We used Pearson's Chi-square or Fisher's Exact tests to compare proportions. The frequency distributions using density plots were constructed for the three breast types. They were compared using the Kruskal Wallis statistic. Kaplan-Meier curves were estimated for the survival analysis and Cox regression was used to identify factors associated with breast cancer survival. Adjusted hazard ratios (AHRs) and their 95% confidence intervals (95% CIs) were computed. RESULTS: A total of 255 cases of breast cancer were included in this study. The mean age was 46.9 years old. Whatever the anatomoclinical type, the density plot curve peaked before the age 40. NT4 and LAC were more frequently observed on the right breast, while IBC occurred on the left breast (p < 0.001). Comorbidity rates were comparable between the three groups. The median survival time was 9 months, and the overall 5-year survival rate was < 40%. Infertility history and IBC had a significant influence on survival, with AHRs of 1,63 [1,01 - 2,63] and 1,52 [1,04 - 2,22] respectively. CONCLUSION: Breast cancer at Gabriel Touré University Hospital is characterized by an early onset and a poor prognosis, suggesting that particular emphasis should be placed on early diagnosis and the quality of management.
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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) | 0.003 | 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".