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Record W4286849373 · doi:10.7202/1084455ar

Shame and Secrecy of Do Not Resuscitate Orders: An Historical Review and Suggestions for the Future

2021· article· en· W4286849373 on OpenAlexaffvenue
John A. O’Connor

Bibliographic record

VenueCanadian Journal of Bioethics · 2021
Typearticle
Languageen
FieldMedicine
TopicPalliative Care and End-of-Life Issues
Canadian institutionsMcMaster University
Fundersnot available
KeywordsCognitive reframingContext (archaeology)Health careSecrecyNegotiationShameLegislationMedicineClosure (psychology)Quality (philosophy)NursingPsychologyMedical emergencySocial psychologyPolitical scienceLaw

Abstract

fetched live from OpenAlex

This paper clarifies some of the longstanding difficulties in negotiating Do Not Resuscitate Orders by reframing the source of the dilemmas as not residing with either the patient or the physician but with their relationship. The recommendations are low cost and low-tech ways of making major improvements to the care and quality of life of the most ill patients in hospital. With impending physician-assisted death legislation there is an urgency to find more efficient and beneficial ways for clinicians and patients to address resuscitation issues at the bedside. Paradigmatic shifts in the nature of the patient-physician relationship will need to be encouraged by the larger community. These encouraged shifts address the concepts of passive/inferior patient – active/superior physician, patient ownership of and access to all their health care information, and treating the patient as a major participant in the delivery of health care. These recommended changes will not in themselves make any patient, physician or other healthcare provider more humane and open in the patient’s final days. The goal, instead, is to have changes to the context of the discussion provide an encouraging environment for more open communication and a balanced relationship among participants with the patient being the most important.

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)0.001
metaresearch head score (Gemma)0.002
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: Not applicable
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.591
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
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 integrity0.0000.000
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 head0.209
GPT teacher head0.427
Teacher spread0.218 · 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
GenreReview

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
Published2021
Admission routes2
Has abstractyes

Explore more

Same venueCanadian Journal of BioethicsSame topicPalliative Care and End-of-Life IssuesFrench-language works237,207