Jaelle Karinna visited doctors four times with heavy bleeding before she was diagnosed with cervical cancerAfter five months of misdiagnoses, Jaelle Karinna was told she had cancer. She was 23, studying psychology at university and working part-time as a receptionist.
A month later, a different doctor diagnosed her with fibroids and hydrosalpinx - fluid blockage in the fallopian tubes. "Cancer in younger age groups is considered rare, and so suspicion of cancer from GPs and other healthcare professionals is lower than it may be for other age groups," she said.
Surprise? What do you expect from gp scammers who don't care about patients, just money? My GP's doctor probably bought her degree in India and doesn't know how to tell the difference between otitis, covid, or laryngitis over the phone of course...
We have summarized this news so that you can read it quickly. If you are interested in the news, you can read the full text here. Read more:
Health Health Latest News, Health Health Headlines
Similar News:You can also read news stories similar to this one that we have collected from other news sources.
Development and validation of an AI-enabled digital breast cancer assay to predict early-stage breast cancer recurrence within 6 years - Breast Cancer ResearchBackground Breast cancer (BC) grading plays a critical role in patient management despite the considerable inter- and intra-observer variability, highlighting the need for decision support tools to improve reproducibility and prognostic accuracy for use in clinical practice. The objective was to evaluate the ability of a digital artificial intelligence (AI) assay (PDxBr) to enrich BC grading and improve risk categorization for predicting recurrence. Methods In our population-based longitudinal clinical development and validation study, we enrolled 2075 patients from Mount Sinai Hospital with infiltrating ductal carcinoma of the breast. With 3:1 balanced training and validation cohorts, patients were retrospectively followed for a median of 6 years. The main outcome was to validate an automated BC phenotyping system combined with clinical features to produce a binomial risk score predicting BC recurrence at diagnosis. Results The PDxBr training model (n = 1559 patients) had a C-index of 0.78 (95% CI, 0.76–0.81) versus clinical 0.71 (95% CI, 0.67–0.74) and image feature models 0.72 (95% CI, 0.70–0.74). A risk score of 58 (scale 0–100) stratified patients as low or high risk, hazard ratio (HR) 5.5 (95% CI 4.19–7.2, p | 0.001), with a sensitivity 0.71, specificity 0.77, NPV 0.95, and PPV 0.32 for predicting BC recurrence within 6 years. In the validation cohort (n = 516), the C-index was 0.75 (95% CI, 0.72–0.79) versus clinical 0.71 (95% CI 0.66–0.75) versus image feature models 0.67 (95% CI, 0.63–071). The validation cohort had an HR of 4.4 (95% CI 2.7–7.1, p | 0.001), sensitivity of 0.60, specificity 0.77, NPV 0.94, and PPV 0.24 for predicting BC recurrence within 6 years. PDxBr also improved Oncotype Recurrence Score (RS) performance: RS 31 cutoff, C-index of 0.36 (95% CI 0.26–0.45), sensitivity 37%, specificity 48%, HR 0.48, p = 0.04 versus Oncotype RS plus AI-grade C-index 0.72 (95% CI 0.67–0.79), sensitivity 78%, specificity 49%, HR 4.6, p | 0.001 versus Oncotype
Source: BioMedCentral - 🏆 22. / 71 Read more »