Surgical Management of Breast Cancer during COVID-19 Pandemic
Bibliographic record
Abstract
Background: Breast cancer is the most common cancer in women worldwide, including Pakistan. Pakistan has the highest prevalence of breast cancer in Asia, according to reports. Objective: To compare the management of breast cancer patients in pre and post COVID-19 timeline, and to observe for any stage progression in both groups. Methods: In this retrospective cohort study, conducted at West Surgical Ward, Mayo Hospital Lahore, from July 2019 to Dec 2020, adult female patients aged 18 years or older with a history of breast cancer surgery or who visited the breast clinic with the a diagnosis of breast cancer were enrolled. We divided them into pre and post COVID-19 subgroups assessed in nine months each. Variables including ASA grade, BMI, referral, size of tumor, number of lymph nodes removed, surgical procedure performed including axillary surgery, TNM stage, type of cancer. extracted from patient charts retrospectively. Univariate regression analysis performed for the progression of the stage. P-value ≤0.05 considered significant. Results: Two hundred and sixty-two (n=262) patients presented during the pre-COVID-19 time (Group A) and one hundred seventy-one (n=171) presented in post-COVID-19 times (Group B). All were female patients, with a mean age of 46.6±10.1 year's in-group A and 45.6±11.8 years in-group B. Significantly, referral patients attendance reduced in group B (10.5%) as compared to group A (70.6%). Metastatic disease (stage 4) also seen in higher number in post COVID-19 (26.2% vs. 35%). Stage progression was real in post Covid-19 group (24.6% vs. 6.5%). Results showed waiting time before visiting the breast clinic (p<0.001) and before radiotherapy (p=0.04) were contributing factors to the progression of the disease. Conclusion: Stage progression is real in the post-COVID-19 subgroup. Waiting time before visiting the breast clinic and before radiotherapy were main factors for the progression of the disease.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 teacher head, 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".