Dataset Information

Breast Cancer Detection: The Development and Pilot Study of a "Tactile Landscape" as a Standardized Testing Tool.

ABSTRACT:

Introduction

There is still a need for competent breast lump detection palpation skills, especially in developing countries. Our goal is to design, develop, and establish a test to determine whether students can, by touch alone, identify and discriminate between a range of different simulated lesions at different adiposity levels.

Methods

Common lesions, breast cancers, and cysts were physically simulated and hidden in a test object referred to as the "tactile landscape" (TL). Ribs, intercostal muscle, and nodularity-normal anatomical features-increased their realistic complexity. Varying depths of features simulated varying degrees of adiposity. A testing protocol was created to determine the testee's ability to identify and discriminate different commonly occurring breast masses using palpation. Five experts (four breast surgeons and one general practitioner) and 20 inexperienced medical students were recruited and tested. Results were compared.

Results

The TL has been based on previously verified breast models and has softness similar to 53% of women's breasts and nodularity similar to 60% as assessed in a breast clinic by breast surgeons. The five experts indicated that the simulated lesions felt like those they might encounter in clinical practice and all of them identified the lesions and nonlesions hidden in the TL 100% correctly, thus indicating the value of the model. In contrast, only one student was able to identify all the lesions. One student identified none of them. The remaining students mean score was 65%.

Conclusions

All students but one performed poorly in comparison to the experts. This indicates that the test could be useful to test students' ability to identify and discriminate breast masses. If successful, it will add previously missing capability to the mix of assessment instruments already used, thus potentially improving clinical breast examination training and assessment.

SUBMITTER: Veitch DE

PROVIDER: S-EPMC6587231 | biostudies-literature | 2019 Jun

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Breast Cancer Detection: The Development and Pilot Study of a "Tactile Landscape" as a Standardized Testing Tool.

Veitch Daisy E DE Bochner Melissa M Molenbroek Johan F M JFM Goossens Richard H M RHM Owen Harry H

Simulation in healthcare : journal of the Society for Simulation in Healthcare 20190601 3

<h4>Introduction</h4>There is still a need for competent breast lump detection palpation skills, especially in developing countries. Our goal is to design, develop, and establish a test to determine whether students can, by touch alone, identify and discriminate between a range of different simulated lesions at different adiposity levels.<h4>Methods</h4>Common lesions, breast cancers, and cysts were physically simulated and hidden in a test object referred to as the "tactile landscape" (TL). Rib ...[more]

PMID: 30969270

Similar Datasets

Project description:Food images are routinely used to investigate the cognitive and neurobiological mechanisms of eating behaviors, but there is a lack of standardized image sets for use in children, which limits cross-study comparisons. To address this gap, we developed a set of age-appropriate images that included 30 high-energy-dense (ED) foods (>2.00 kcal/g), 30 low-ED foods (<1.75 kcal/g), and 30 office supplies photographed in two amounts (i.e., "larger" and "smaller"). Preliminary testing was conducted with children (6-10 years) to assess recognition, emotional valence (1 = very sad, 5 = very happy), and excitability (1 = very bored, 5 = very excited). After the initial testing, 10 images with low recognition were replaced; thus, differences between Image Set 1 and Image Set 2 were analyzed. Thirty (n = 30, mean age 8.3 ± 1.2 years) children rated Set 1, and a different cohort of 29 children (mean age 8.1 ± 1.1 years) rated Set 2. Changes made between image sets improved recognition of low-ED foods (Set 1 = 88.3 ± 10.5% vs. Set 2 = 95.6 ± 10.6%; p < 0.0001) and office supplies (83.7 ± 10.5 vs. 93.0 ± 10.6%; p < 0.0001). For the revised image set, children recognized more high-ED foods (98.4 ± 10.6%) than low-ED foods (95.6 ± 10.6%; p < 0.05) and office supplies (93.0 ± 10.6%; p < 0.0001). Recognition also improved with age (p < 0.001). Excitability and emotional valence scores were greater for high-ED foods compared with both low-ED foods and office supplies (p < 0.0001 for both). However, child fullness ratings influenced the relationship between excitability/emotional valence and category of item (p < 0.002). At the lowest fullness level, high-ED foods were rated the highest in both excitability and emotional valence, followed by low-ED foods and then office supplies. At the highest fullness level, high-ED foods remained the highest in excitability and emotional valence, but ratings for low-ED foods and office supplies were not different. This suggests that low-ED foods were more exciting and emotionally salient (relative to office supplies) when children were hungry. Ratings of recognition, excitability, and emotional valence did not differ by image amount. This new, freely available, image set showed high recognition and expected differences between image category for emotional valence and excitability. When investigating children's responsiveness to food cues, specifically energy density, it is essential for investigators to account for potential influences of child age and satiety level.

Project description:BackgroundThe predominant oncologist-led model in many countries is unsustainable to meet the needs of a growing cohort of breast cancer survivors (BCS). Despite available alternative models, adoption rates have been poor. To help BCS navigate survivorship care, we aimed to systematically develop a decision aid (DA) to guide their choice of follow-up care model and evaluate its acceptability and usability among BCS and health care providers (HCPs).MethodsWe recruited BCS aged ≥ 21 years who have completed primary treatment and understand English. BCS receiving palliative care or with cognitive impairment were excluded. HCPs who routinely discussed post-treatment care with BCS were purposively sampled based on disciplines. Each participant reviewed the DA during a semi-structured interview using the 'think aloud' approach and completed an acceptability questionnaire. Descriptive statistics and directed content analysis were used.ResultsWe conducted three rounds of alpha testing with 15 BCS and 8 HCPs. All BCS found the final DA prototype easy to navigate with sufficient interactivity. The information imbalance favouring the shared care option perceived by 60% of BCS in early rounds was rectified. The length of DA was optimized to be 'just right'. Key revisions made included (1) presenting care options side-by-side to improve perceived information balance, (2) creating dedicated sections explaining HCPs' care roles to address gaps in health system contextual knowledge, and (3) employing a multicriteria decision analysis method for preference clarification exercise to reflect the user's openness towards shared care. Most BCS (73%) found the DA useful for decision-making, and 93% were willing to discuss the DA with their HCPs. Most HCPs (88%) agreed that the DA was a reliable tool and would be easily integrated into routine care.ConclusionsOur experience highlighted the need to provide contextual information on the health care system for decisions related to care delivery. Developers should address potential variability within the care model and clarify inherent biases, such as low confidence levels in primary care. Future work could expand on the developed DA's informational structure to apply to other care models and leverage artificial intelligence to optimize information delivery.

Dataset Information

Breast Cancer Detection: The Development and Pilot Study of a "Tactile Landscape" as a Standardized Testing Tool.

Introduction

Methods

Results

Conclusions

Publications

Breast Cancer Detection: The Development and Pilot Study of a "Tactile Landscape" as a Standardized Testing Tool.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets