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Life and death decisions for incompetent patients: determining best interests – the Irish perspective

2010· article· en· W1931507552 on OpenAlexaff
Kathryn Armstrong, CA Ryan, Colin P. Hawkes, Annie Janvier, Eugene Dempsey

Bibliographic record

VenueActa Paediatrica · 2010
Typearticle
Languageen
FieldMedicine
TopicPalliative Care and End-of-Life Issues
Canadian institutionsCentre Hospitalier Universitaire Sainte-Justine
Fundersnot available
KeywordsResuscitationMedicinePerspective (graphical)NeonatologyResuscitation OrdersBest interestsIrishIntensive care medicineMedical emergencyCardiopulmonary resuscitationEmergency medicinePregnancy

Abstract

fetched live from OpenAlex

AIMS: To determine whether healthcare providers apply the best interest principle equally to different resuscitation decisions. METHODS: An anonymous questionnaire was distributed to consultants, trainees in neonatology, paediatrics, obstetrics and 4th medical students. It examined resuscitation scenarios of critically ill patients all needing immediate resuscitation. Outcomes were described including survival and potential long-term sequelae. Respondents were asked whether they would intubate, whether resuscitation was in the patients best interest, would they accept surrogate refusal to initiate resuscitation and in what order they would resuscitate. RESULTS: The response rate was 74%. The majority would wish resuscitation for all except the 80-year-old. It was in the best interest of the 2-month-old and the 7-year-old to be resuscitated compared to the remaining scenarios (p value <0.05 for each comparison). Approximately one quarter who believed it was in a patient best interests to be resuscitated would nonetheless accept the family refusing resuscitation. Medical students were statistically more likely to advocate resuscitation in each category. CONCLUSION: These results suggest resuscitation is not solely related to survival or long-term outcome and the best interest principle is applied differently, more so at the beginning of life.

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)0.008
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.246
Threshold uncertainty score0.925

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.008
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 head0.098
GPT teacher head0.402
Teacher spread0.304 · 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 designObservational
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

Citations18
Published2010
Admission routes1
Has abstractyes

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