Bibliographic record
Abstract
Abstract Background Patients require information to make informed decisions and consent to medical treatment. Shared decision making (SDM) is a methodology that promotes a patient-centred approach to informed consent and demonstrates respect for autonomy Purpose The purpose of this paper is to critically review the legal and ethical issues relevant to Canadian and UK informed consent and SDM practices and how these processes relate to current palliative care practices, with a particular emphasis on radiation therapy. Methodology A review of the English literature from 2003 to 2013 was performed using the databases PubMed (NML), OVID Medline and Google Scholar. Results and Conclusions The literature identifies that palliative cancer patients desire the opportunity to be involved with decision-making discussions, which has shown to increase knowledge and result in better health-related outcomes. However, ethical and legal issues regarding the practicality of including this patient population in SDM discussions raises questions about validity of consent. For SDM to be considered a valid methodology to obtain informed consent, open and honest communication between the patient and multidisciplinary team is essential. Treatment options for palliative cancer patients are often complex and SDM allows healthcare professionals and patients to exchange information and negotiate feasible treatment options based on medical expertise and patient preferences. Legal frameworks have defined current standards of practice for various healthcare professions, including radiation therapy. Radiation therapists, as members of the multidisciplinary team, are currently key contributors in providing information to patients regarding the radiotherapy process. Individuals working within advanced practice roles have the ability to develop skills once considered to be within medical domains and have begun to incorporate the delegated act of obtaining informed consent into practice which has shown to increase professional autonomy, accountability and improves patient-centred care.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".