Why so late? Presentation delay in locally advanced breast cancer (LABC)
Bibliographic record
Abstract
712 Background: While there have been marked improvements in the prognosis of women with early breast cancer, these gains have not been echoed to the same degree in patients with LABC. Accounting for 10 to 30% of all new primary breast cancers, 5-year survival for LABC remains poor at around 55%. Research suggests that many women with breast cancer have delayed presentation of symptomatic breast cancer, with up to 30% waiting at least 3 months before presenting to a health care provider. Delays may be associated with more advanced lesions & lower survival rates. The characteristics of these patients & reasons for their delaying behaviour are poorly understood. Methods: From October 2001 to September 2004, all new patients referred to our LABC clinic had data collected asking what date she discovered her breast cancer symptom, the type of symptom, her appraisal of that symptom, & the date of initial presentation. Sociodemographic data were also collected. Results: 69 patients were referred with LABC. 98% were female. 62% were Caucasian, 16% Asian, & 13% Black. Median age at presentation 42 years (range 27–83). 55% were married. Initial symptoms were: painless breast lump (59%), axillary mass (9%), breast erythema (9%), nipple inversion (4%), or other symptom (19%). Median time from symptom discovery to presentation to health care provider was 3 months (range 0–416 weeks). Factors that affected a timely diagnosis were: mistaken attribution of symptom as benign process, fear of cancer, belief that breast cancer was unlikely in the absence of a positive family history, belief that it is better “not to know”, decision to pursue complementary therapies first, concern about significant other’s ability to cope, & competing life demands. Conclusions: Late-stage presentation of breast cancer is associated with poor survival. 50% of newly diagnosed LABC patients in this study waited over three months before presenting to a health care provider with their breast cancer symptoms. To reduce morbidity & mortality for all patients with breast cancer, further research is necessary to develop a clear understanding of both the factors influencing when women seek care & whether there are opportunities for earlier intervention. No significant financial relationships to disclose.
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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 | 0.001 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".