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Record W2167597361 · doi:10.1017/s1478951505050029

Identification of patients with noncancer diseases for palliative care services

2005· article· en· W2167597361 on OpenAlexaff
Carol Grbich, Ian Maddocks, Deborah Parker, Margaret Brown, Eileen Willis, Neil Piller, Anne Hofmeyer

Bibliographic record

VenuePalliative & Supportive Care · 2005
Typearticle
Languageen
FieldMedicine
TopicPalliative Care and End-of-Life Issues
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsPalliative careMedicineScale (ratio)Adaptation (eye)Identification (biology)GerontologyFamily medicineNursingPsychology

Abstract

fetched live from OpenAlex

OBJECTIVE: To identify criteria for measuring the eligibility of patients with end-stage noncancer diseases for palliative care services in Australian residential aged care facilities. METHODS: No validated set if guidelines were available so five instruments were used: an adaptation of the American National Hospice Association Guidelines; a recent adaptation of the Karnofsky Performance Scale; the Modified Barthel Index; the Abbey Pain Score for assessment of people who are nonverbal and a Verbal Descriptor Scale, also for pain measurement. In addition, nutritional status and the presence of other problematic symptoms and their severity were also sought. RESULTS: The adapted American National Hospice Association Guidelines provided an initial indicative framework and the other instruments were useful in providing confirmatory data for service eligibility and delivery.

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)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.006
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.001

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 head0.039
GPT teacher head0.385
Teacher spread0.346 · 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 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

Citations51
Published2005
Admission routes1
Has abstractyes

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