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Record W1673230826 · doi:10.1016/j.ijgo.2015.07.001

Patients’ refusal of recommended treatment

2015· article· en· W1673230826 on OpenAlexaff
Bernard M. Dickens, Rebecca J. Cook

Bibliographic record

VenueInternational Journal of Gynecology & Obstetrics · 2015
Typearticle
Languageen
FieldMedicine
TopicPharmaceutical studies and practices
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMedicineFamily medicine

Abstract

fetched live from OpenAlex

When patients require information to decide whether to accept recommended treatments, a question in both law and ethics is whether the same information is adequate whether they consent or refuse, or whether refusal requires more or repeated information. Refusals of recommended treatment can carry increased risks for patients' well-being and so require more emphatic disclosure without imposing pressure. A related question is whether guardians of dependents who would decline recommended treatment for themselves--for instance on religious grounds--can similarly decline it for their dependents. When pregnant women, children, and adolescents are able to give consent for recommended treatment, the question arises whether they are equally competent to refuse it and prevent their decisions being overridden by guardians or courts. Consenting to and refusing medical treatments recommended in one's own or dependents' best interests might not be the same sorts of decisions and could require different levels of disclosure and capacity.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.007
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.891
Threshold uncertainty score0.815

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.112
GPT teacher head0.403
Teacher spread0.290 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations14
Published2015
Admission routes1
Has abstractyes

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