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Record W2947917222 · doi:10.15273/dmj.vol45no2.8993

Decision-making through the lens of a pediatric cancer case

2019· article· en· W2947917222 on OpenAlexafffundvenue
Tamara Selman

Bibliographic record

VenueDalhousie Medical Journal · 2019
Typearticle
Languageen
FieldMedicine
TopicEthics and Legal Issues in Pediatric Healthcare
Canadian institutionsDalhousie University
FundersDalhousie University
KeywordsBeneficenceAutonomyPaternalismBioethicsCompetence (human resources)Best interestsPsychologyPediatric cancerPersonal autonomyMedicineLawCancerSocial psychologyPolitical science

Abstract

fetched live from OpenAlex

It is crucial to obtain a competent individual’s informed consent in any medical process, including cancer treatments. However, when it comes to incompetent children, it seems to be favourable, but not necessary, to obtain their assent in medical practice.1 This paper considers Christine Harrison’s example of Samantha, an eleven-year-old girl that was treated for osteosarcoma in her left arm. Samantha had previously been treated by amputation and a course of chemotherapy. This cancer later metastasized to her lungs, decreasing her chances of remission with aggressive treatment to 20%. Although she wanted to refuse treatment, she was deemed incompetent to make decisions about her cancer care, and her parents adamantly wanted her to continue treatment.7 This paper considers physicians’ moral obligations in pediatric cancer cases such as Samantha’s. I will define assent, the principles of autonomy, beneficence, and competence as it pertains to children. I consider arguments of two opposing views–a child’s rights view that argues in favour of Samantha’s decision, and a paternalistic view that opposes her. After reviewing the bioethical literature on the risks and benefits of children’s decision making in health care, I argue that Samantha’s wishes to stop treatment ought to be respected.Throughout the paper, I will use the bioethical principles of respect for autonomy and beneficence to defend my position. Finally, I address potential objections my position may face and conclude.

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)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesResearch integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.609
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
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 integrity0.0000.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.038
GPT teacher head0.398
Teacher spread0.359 · 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.

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

Citations0
Published2019
Admission routes3
Has abstractyes

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