articles+ search results

Prediction, Palliative and Supportive Care (MP Davis, Section Editor), Neoplasms, Communication, Uncertainty, Prognosis, and Palliative care

Opinion statement Patients with advanced cancer and their families commonly seek information about prognosis to aid decision-making in medical (e.g. surrounding treatment), psychological (e.g. saying goodbye), and social (e.g. getting affairs in order) domains. Oncologists therefore have a responsibility to identify and address these requests by formulating and sensitively communicating information about prognosis. Current evidence suggests that clinician predictions are correlated with actual survival but tend to be overestimations. In an attempt to cultivate prognostic skills, it is recommended that clinicians practice formulating and recording subjective estimates of prognosis in advanced cancer patient’s medical notes. When possible, a multi-professional prognostic estimate should be sought as these may be more accurate than individual predictions alone. Clinicians may consider auditing the accuracy of their predictions periodically and using feedback from this process to improve their prognostic skills. Clinicians may also consider using validated prognostic tools to complement their clinical judgements. However, there is currently only limited evidence about the comparative accuracy of different prognostic tools or the extent to which these measures are superior to clinical judgement. Oncologists and palliative care physicians should ensure that they receive adequate training in advanced communication skills, which builds upon their pre-existing skills, to sensitively deliver information on prognosis. In particular, clinicians should acknowledge their own prognostic uncertainty and should emphasise the supportive care that can continue to be provided after stopping cancer-directed therapies.

Research Article, Withholding treatment, Physician-patient relations, Decision making, Defense mecanisms, Medicine(all), Neoplasms, Uncertainty, Ethics, Advance care planning, Chemotherapy, and Palliative care

Background Little is known about what is at stake at a subjective level for the oncologists and the advanced cancer patients when they face the question whether to continue, limit or stop specific therapies. We studied (1) the frequency of such questioning, and (2) subjective determinants of the decision-making process from the physicians’ and the patients’ perspectives. Methods (1) All hospitalized patients were screened during 1 week in oncology and/or hematology units of five institutions. We included those with advanced cancer for whom a questioning about the pursuit, the limitation or the withholding of specific therapies (QST) was raised. (2) Qualitative design was based on in-depth interviews. Results In conventional units, 12.8 % of cancer patients (26 out of 202) were concerned by a QST during the study period. Interviews were conducted with all physicians and 21 advanced cancer patients. The timing of this questioning occurred most frequently as physicians estimated life expectancy between 15 days and 3 months. Faced with the most frequent dilemma (uncertain risk-benefit balance), physicians showed different ways of involving patients. The first two were called the “no choice” models: 1) trying to resolve the dilemma via a technical answer or a “wait-and-see” posture, instead of involving the patients in the questioning and the thinking; and 2), giving a “last minute” choice to the patients, leaving to them the responsibility of the decision. In a third model, they engaged early in shared reflections and dialogue about uncertainties and limits with patients, proxies and care teams. These schematic trends influenced patients’ attitudes towards uncertainty and limits, as they were influenced by these ones. Individual and systemic barriers to a shared questioning were pointed out by physicians and patients. Conclusions This study indicate to what extent these difficult decisions are related to physicians’ and patients’ respective and mutually influenced abilities to deal with and share about uncertainties and limits, throughout the disease trajectory. These insights may help physicians, patients and policy makers to enrich their understanding of underestimated and sensitive key issues of the decision-making process.

Object recognition, Dempster-Shafer theory, Neoplasms, Classification, Datenfusion, DDC 004 / Data processing & computer science, Information fusion, Incremental learning, Hierarchical neural networks, Uncertainty, ddc:004, and Feature selection

This work offers a novel approach for solving muti-class object recognition problems by dividing the complex task into several hierarchically structured sub-problems that are easier to solve. The work at hand covers several aspects of object recognition with hierarchical neural networks: 1. Generation of appropriate classifier hierarchies; 2. Evaluation of hierarchies (information fusion); 3. Selection of suitable features for the classification; 4. Extensibility/adaptivity of the hierarchies; 5. Generation of similarity preserving sparse binary codes. The selection of suitable feature types for the classification of objects from various domains is an important aspect for the recognition of three-dimensional objects. In the context of this work diverse feature types were deployed and evaluated. Different strategies for the retrieval of the combined classification result from the classifier hierarchies were developed and evaluated. The most promising approaches were the retrieval strategy similar to decision trees, the retrieval strategy utilizing the Dempster-Shafer evidence theory as well as the retrieval strategy utilizing similarity preserving codes and inter-state decision templates. The generation of similarity preserving sparse binary codes is an additional aspect of the object recognition with hierarchical neural network classifiers. Within the scope of this work several strategies for the generation of such codes based on the activation of the neural classifiers within the hierarchy were developed and evaluated. Another focus was the incremental learning of hitherto unknown objects. In complex real-world environments this is an often desirable capability as the confrontation with so far unfamiliar objects is very likely. Within the scope of this work a method for subsequently learning new objects in an efficient manner without negatively influencing the classification performance of previously learnt objects.