Dataset Information

Local and Systemic Therapies for Breast Cancer Patients: Reducing Short-term Symptoms with the Methods of Integrative Medicine.

ABSTRACT: With improved prognosis due to advances in the diagnosis and therapy of breast cancer, physicians and therapists now focus on aspects such as quality of life and the management of side effects from breast cancer treatment. Therapy- and disease-related side effects often reduce the patient's quality of life and can place a further burden on patients, with non-compliance or discontinuation of therapy a potential consequence. Study data have shown that therapy- and disease-related side effects can be reduced using the methods of integrative medicine. Reported benefits include improving patients' wellbeing and quality of life, reducing stress, and improving patients' mood, sleeping patterns and capacity to cope with disease. Examining the impact of integrative medicine on the side effects of cancer treatment would be beyond the scope of this review. This article therefore looks at short-term side effects of cancer treatment which are usually temporary and occur during or after local and systemic therapy. The focus is on mind-body medicine, acupuncture and classic naturopathic treatments developed by Sebastian Kneipp as complementary therapies. The latter includes hydrotherapy, phytotherapy, nutritional therapy, exercise therapy and a balanced lifestyle.

SUBMITTER: Hack CC

PROVIDER: S-EPMC4520960 | biostudies-literature | 2015 Jul

REPOSITORIES: biostudies-literature

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Local and Systemic Therapies for Breast Cancer Patients: Reducing Short-term Symptoms with the Methods of Integrative Medicine.

Hack C C CC Voiß P P Lange S S Paul A E AE Conrad S S Dobos G J GJ Beckmann M W MW Kümmel S S

Geburtshilfe und Frauenheilkunde 20150701 7

With improved prognosis due to advances in the diagnosis and therapy of breast cancer, physicians and therapists now focus on aspects such as quality of life and the management of side effects from breast cancer treatment. Therapy- and disease-related side effects often reduce the patient's quality of life and can place a further burden on patients, with non-compliance or discontinuation of therapy a potential consequence. Study data have shown that therapy- and disease-related side effects can ...[more]

PMID: 26257404

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Project description:Forecasting healthcare demand is essential in epidemic settings, both to inform situational awareness and facilitate resource planning. Ideally, forecasts should be robust across time and locations. During the COVID-19 pandemic in England, it is an ongoing concern that demand for hospital care for COVID-19 patients in England will exceed available resources. We made weekly forecasts of daily COVID-19 hospital admissions for National Health Service (NHS) Trusts in England between August 2020 and April 2021 using three disease-agnostic forecasting models: a mean ensemble of autoregressive time series models, a linear regression model with 7-day-lagged local cases as a predictor, and a scaled convolution of local cases and a delay distribution. We compared their point and probabilistic accuracy to a mean-ensemble of them all, and to a simple baseline model of no change from the last day of admissions. We measured predictive performance using the Weighted Interval Score (WIS) and considered how this changed in different scenarios (the length of the predictive horizon, the date on which the forecast was made, and by location), as well as how much admissions forecasts improved when future cases were known. All models outperformed the baseline in the majority of scenarios. Forecasting accuracy varied by forecast date and location, depending on the trajectory of the outbreak, and all individual models had instances where they were the top- or bottom-ranked model. Forecasts produced by the mean-ensemble were both the most accurate and most consistently accurate forecasts amongst all the models considered. Forecasting accuracy was improved when using future observed, rather than forecast, cases, especially at longer forecast horizons. Assuming no change in current admissions is rarely better than including at least a trend. Using confirmed COVID-19 cases as a predictor can improve admissions forecasts in some scenarios, but this is variable and depends on the ability to make consistently good case forecasts. However, ensemble forecasts can make forecasts that make consistently more accurate forecasts across time and locations. Given minimal requirements on data and computation, our admissions forecasting ensemble could be used to anticipate healthcare needs in future epidemic or pandemic settings.

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Dataset Information

Local and Systemic Therapies for Breast Cancer Patients: Reducing Short-term Symptoms with the Methods of Integrative Medicine.

Publications

Local and Systemic Therapies for Breast Cancer Patients: Reducing Short-term Symptoms with the Methods of Integrative Medicine.

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