Consent to Medical Treatment: What about the Adult Patient who is Incapable of Providing a Valid Consent?
Bibliographic record
Abstract
Consent to medical treatment is a principle that is increasingly gaining attention in health care systems across the world. For consent to be valid, five essential elements must exist. The patient must have the mental ‘capacity’ to provide consent, he or she must receiveadequate and accurate information, understand the information disclosed, make a decision voluntarily and without coercion, and then authorize the treatment. In an article on ‘consent to medical treatment’ in a previous edition of this journal, I outlined how the Ghanaian courts may resolve consent related information disclosure disputes. That article dealt with the provision of information to the adult patient who has the mental capacity to provide lawful consent to medical treatment. A likely question that an interested reader of that article may ask is; ‘what are the legal provisions in Ghana for treating patients who lack the capacity to provide lawful consent to medical treatment as a result of factors such as a young age, disease, severe brain or mental illness or incapacity such as occurs in severe dementia, severe learning disabilities, and being unconscious? The purpose of this article is to attempt to answer sucha question. The scope of the article is limited to the management of the adult patient without the mental capacity to consent to medical treatment, which for the purpose of this article, in Ghana, refers to any individual aged 18 years or older who lacks the mentalcapacity.
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.038 | 0.126 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.047 |
| Scholarly communication | 0.008 | 0.022 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.019 | 0.025 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".