Abstract P4-10-09: Delay in diagnosis of breast cancer in Mexican young women: Report of the “Joven y Fuerte” prospective cohort pilot phase
Bibliographic record
Abstract
Abstract Background: Delay in diagnosis and treatment initiation of breast cancer (BC) has been associated with advanced stages and poor outcome. In developed countries, age has not been solely reported as an independent predictor of diagnosis delay. In Mexico, median time since tumor detection to treatment initiation is about 7 months, but young women are underrepresented in these studies. We aim to describe time intervals related to diagnosis in Mexican young women with BC (YWBC). Methods: Newly diagnosed YWBC were invited to participate as part of this prospective cohort. Patient accrual began in November 2014 at two public cancer centers in Mexico. Patients completed self-report surveys including questions regarding mode of detection, time from first symptom to medical appointment (patient interval) and time from first symptom to diagnosis (total interval). Pearson chi-square tests were used to examine the effects of patient and clinical characteristics on patient interval and clinical stage. Results: 96 YWBC with median age at diagnosis of 35 y (range 21-40) were enrolled in our pilot phase. 82.3% had tumor detected by self or partner. 62.5% of YWBC were diagnosed as locally advanced disease (IIB-IIIC). Median tumor size was 3.5 cm (0.5-12.0), with node involvement in 66.7%. 53.1% of YWBC had a patient interval of <6 months, but roughly 27.1% had a total interval <6 months. While only 13.5% had a patient interval >12 months, 39.6% reached a total interval >12 months. Patient interval and clinical stage were not significantly associated with occupation, education, marital status, current partner or method of detection. N(%)TimePatient intervalTotal interval<1 month29 (30.2)7 (7.3)1-3 months18 (18.8)9 (9.4)4-6 months4 (4.2)10 (10.4)7-12 months10 (10.4)24 (25.0)>12 months13 (13.5)38 (39.6)No symptoms0 (0.0)3 (3.1)NA22 (22.9)5 (5.2)Method of Detection Patient/Partner detected tumor79 (82.3)Clinical detection0 (0.0)Image detected9 (94)NA8 (8.3)Clinical stage 02 (2.1)IA13 (13.5)IB1 (1.0)IIA14 (14.6)IIB17 (17.7)IIIA28 (29.2)IIIB8 (8.3)IIIC7 (7.3)IV6 (6.2) Conclusions: In this cohort, most patients had a greater total delay than previously reported in Mexico, possibly attributed to long health-system intervals, which could contribute to worse outcomes in YWBC. The prospective nature of this study allows the recollection of biologic characteristics, treatment scheme and adherence to treatment, to determine their impact on clinical outcome besides diagnosis delay. “Joven & Fuerte”, the first dedicated program for the care of young breast cancer patients in Latin America, aims to develop YWBC-tailored interventions to early diagnose or “downstage” BC among young women by endorsing patient navigation, increasing general population awareness and improving providers' knowledge in low-middle income countries, such as Mexico. Citation Format: Castro-Sanchez A, Barragan-Carrillo R, Miaja M, Platas A, Martinez Cannon BA, Fonseca A, Vega Y, Bukowski A, Chapman J-A, Goss P, St. Louis J, Bargallo-Rocha JE, Mohar A, Peña-Curiel O, Villarreal-Garza CM. Delay in diagnosis of breast cancer in Mexican young women: Report of the “Joven y Fuerte” prospective cohort pilot phase [abstract]. In: Proceedings of the 2017 San Antonio Breast Cancer Symposium; 2017 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2018;78(4 Suppl):Abstract nr P4-10-09.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".