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A subdivision of microbiology that involves the identification, classification, and characterisation of bacteria.
Baseline data provide information about participants as they enter a trial and before they receive any treatment.
Baseline data collection may take the form of interviews, questionnaires, physical examinations, laboratory tests, or other procedures. Baseline data include demographics (such as age, gender), patient characteristics (such as height, weight, blood pressure), and measurements specific to the study (such as disease characteristics or previous treatment).
Beneficence is a concept in research ethics that states that researchers should have the welfare of the research participant as a goal in any clinical trial or other research study. In public health, beneficence implies acting in the best interest of the population or society as a whole.
Medicines almost always carry risks as well as benefits, so practising beneficence in clinical trials is not straight forward. An analysis of the risks as well as the benefits is required in each case.
Benefit is a positive outcome (such as the relief of symptoms, cure, or prevention) from using a treatment or taking part in a study. The benefits of taking part in research may include helping others by participating in medical research, close monitoring by health professionals and experts, or getting access to an effective treatment before it is made available to the wider patient population.
In medicines R&D, benefit-risk assessment is the continuous examination of the favourable and unfavourable results of a specific treatment to determine whether its benefits outweigh its risks in a -specific condition. It takes into account the evidence on safety and efficacy, as well as other factors like the nature and severity of the condition the medicine is intended to treat or prevent.
Best supportive care
Best supportive care (BSC) is the treatment of choice when a cure is not achievable with existing treatments. It involves the management of disease-related symptoms.
In clinical trails, bias is the systematic deviation from true values of treatment effect through the intentional or unintentional adjustment of results. Bias can result from aspects of trial design, the way a trial is carried, or the way the results are analysed or evaluated.
Bias can be 'operational' - when it arises because of the way a trial is carried out or 'statistical' - when it arises because of trial design or the way results are analysed or evaluated
For example, poor trial design might mean that participants at lower risk of experiencing a symptom are placed in one treatment arm as opposed to another treatment arm. Excluding data from certain participants because of knowledge of their outcomes would also cause bias in a trial.
The most important design techniques for avoiding bias in clinical trials are blinding and randomisation. The potential effect of bias should also be taken into account during statistical analysis of trial data.
Big data in the health sector is the combination and analysis of very large and diverse sets of data, such as non-health and health data, ongoing generation of information about the real-world use of medicines, patient-generated data from social media, and wearable devices.
The endpoint of a clinical trial is the measurement (such as change in tumour size) used to decide whether there is a significant difference between different arms of the trial (for example, whether a medicine under study is having a positive effect).
There are different types of endpoints. A binary endpoint is defined by whether an event has occurred or not (for example, the relief of symptoms, or occurrence of disease symptoms). It does not imply a certain magnitude of an effect. It is a Yes/No construct.