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Record W2084044584 · doi:10.1136/jme.2008.024752

Propranolol, post-traumatic stress disorder and narrative identity

2008· article· en· W2084044584 on OpenAlexaffabout
Jennifer Bell

Bibliographic record

VenueJournal of Medical Ethics · 2008
Typearticle
Languageen
FieldMedicine
TopicEmpathy and Medical Education
Canadian institutionsDalhousie University
Fundersnot available
KeywordsNeuroethicsNarrativeBioethicsPropranololIdentity (music)Traumatic stressPsychotherapistMedicineQuality of life (healthcare)PsychiatryPsychologyTraumatic memoriesNarrative therapyLawPolitical scienceNeuroscienceAnesthesiaAesthetics

Abstract

fetched live from OpenAlex

FUNDING: Research funded by Canadian Institutes of Health Research, NNF 80045, States of Mind: Emerging Issues in Neuroethics. While there are those who object to the prospective use of propranolol to prevent or treat post-traumatic stress disorder (PTSD), most obstreperous among them the President's Council on Bioethics, the use of propranolol can be justified for patients with severe PTSD. Propranolol, if effective, will alter the quality of certain memories in the brain. But this is not a serious threat to the self understood in terms of narrative identity. A narrative identity framework acknowledges that memory is always being subtly altered or modified. For severe cases of PTSD propranolol may help victims who don't respond to any other therapy or therapy combination regain their authentic self-narrative and engage once more in life activities. For those whose symptoms are not so severe the potential risks and side-effects of the drug may outweigh the benefits. Patients and family members should be allowed to decide, in consultation with their physician, whether this drug is appropriate in their case.

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)0.003
metaresearch head score (Gemma)0.024
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.271
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.024
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.004
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 head0.058
GPT teacher head0.399
Teacher spread0.341 · 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 designQualitative
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

Citations20
Published2008
Admission routes2
Has abstractyes

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