Breast cancer diagnostic delays in pakistan: a looming epidemic threat
Bibliographic record
Abstract
Breast cancer is a major contributing factor to the mortality and morbidity burden among the female population in Asia. In 2020, a total of 1.2 million newly diagnosed breast cancer cases and an estimated 3.5 million deaths due to breast cancer were reported in Asia (International Agency for Research on Cancer, 2020a). In particular, Pakistan notably reported the highest proportion of breast cancer cases in Asia. Research estimates that one out of nine women in Pakistan is at a high risk of suffering from breast cancer in their lifetime. The constant growth in breast cancer rates in Pakistan indicates that breast cancer is rapidly reaching epidemic proportions and poses an urgent challenge to Pakistan's public health system. Due to system-level and patient-level delay factors, Pakistani women often seek medical care for breast carcinoma at an advanced stage of the disease, whereby survival chances are minimal. The key to mitigating the breast cancer burden in Pakistan is to foster early detection programs among Pakistani women. This review aims to examine the root causes of delayed detection of breast cancer in Pakistani women, emphasize the pivotal role of early detection in individuals' and populations' health promotion, and highlight nursing implications in promoting breast cancer early detection programs. A comprehensive literature search was conducted in databases including CINAHL, Google Scholar, and Scopus. The review consists of articles from 2005 to 2020 published in the English language only. Furthermore, the study also highlights the need for context-specific and culturally sensitive early breast cancer detection programs to potentially reduce barriers in the uptake of screening services among Pakistani women.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".