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Record W592113 · doi:10.1093/pch/13.3.181

The ethics of using innovative therapies in the care of children

2008· article· en· W592113 on OpenAlexaff
Ayman Al Eyadhy, Saleem Razack

Bibliographic record

VenuePaediatrics & Child Health · 2008
Typearticle
Languageen
FieldMedicine
TopicEthics and Legal Issues in Pediatric Healthcare
Canadian institutionsMcGill UniversityMontreal Children's Hospital
Fundersnot available
KeywordsAutonomyMedicineContext (archaeology)Engineering ethicsCorporate governanceResearch ethicsPsychotherapistPsychologyPolitical sciencePsychiatryBusinessLawEngineering

Abstract

fetched live from OpenAlex

An innovative therapy is a newly introduced or modified therapy with unproven effect or side effect, and is undertaken in the best interest of the patient. The ethical use of innovative therapies has been controversial. In paediatrics, the conflict between withholding potential rescue therapy and protecting a vulnerable population's rights and welfare must be considered. Therefore, it is necessary to ensure that this innovation is conducted within an ethical framework that recognizes that the therapy is not standard. This should integrate the patient's autonomy, the role of the institution, professional consensus and innovation evaluation. Innovative therapy represents a justifiable departure from inferior conventional therapy in the absence of an accepted standard therapy. Innovation shares with research its experimental nature, but differs from research in its goal and context that exempts innovative therapy from direct governance by research ethics board. Innovative therapy is part of the continuum of hypothesis generation in the advancement of medical knowledge, and its evaluation is a transforming point for clinical research.

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 imitation

Not 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.

metaresearch head score (Codex)0.091
metaresearch head score (Gemma)0.093
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.091
Threshold uncertainty score0.480

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0910.093
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0070.057
Scholarly communication0.0110.007
Open science0.0020.009
Research integrity0.0150.016
Insufficient payload (model declined to judge)0.0010.001

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.

Opus teacher head0.064
GPT teacher head0.388
Teacher spread0.323 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations15
Published2008
Admission routes1
Has abstractyes

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