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Record W115172967 · doi:10.1177/082585971102700203

Resource Use and costs of End-Of-Life/Palliative Care: Ontario Adult Cancer Patients Dying during 2002 and 2003

2011· article· en· W115172967 on OpenAlexafffundabout
Hugh Walker, Mark Anderson, Farah Farahati, Doris Howell, S. Lawrence Librach, Amna Husain, Jonathan Sussman, Raymond Viola, Rinku Sutradhar, Lisa Barbera

Bibliographic record

VenueJournal of Palliative Care · 2011
Typearticle
Languageen
FieldMedicine
TopicPalliative Care and End-of-Life Issues
Canadian institutionsJuravinski Cancer CentreUniversity of TorontoUniversity Health NetworkInstitute for Clinical Evaluative SciencesEngineers Without Borders CanadaMcMaster UniversityQueen's University
FundersOntario Ministry of Health and Long-Term CareInstitute for Clinical Evaluative SciencesCancer Care Ontario
KeywordsPalliative careMedicineChristian ministryCancerEnd-of-life careHealth careCancer registryFamily medicineEmergency medicineGerontologyNursingInternal medicine

Abstract

fetched live from OpenAlex

The objective of this study is to estimate the direct medical cost of end-of-life and palliative (EOL/PAL) care for cancer patients during the last six months of their lives--or, during the period from diagnosis to death, if briefer--in 2002 and 2003, in Ontario, Canada. A linkage of cancer registry and administrative data is used to determine the costs of health care resources used during the EOL/PAL care period. Costs are analyzed by cancer diagnosis, location of death, and type of service. The total Ontario Ministry of Health-funded cost of EOL/PAL care for cancer patients is estimated to be about CAD$544 million per year, with an average per patient cost of about $25,000 in 2002-2003. Our results suggest that acute care consumes 75 percent of EOL/PAL funding and that only a small proportion of health care services used by EOL/PAL care cancer patients is likely to be formal palliative care.

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)0.000
metaresearch head score (Gemma)0.004
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 score0.968
Threshold uncertainty score0.229

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.004
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.107
GPT teacher head0.353
Teacher spread0.246 · 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

Citations67
Published2011
Admission routes3
Has abstractyes

Explore more

Same venueJournal of Palliative CareSame topicPalliative Care and End-of-Life IssuesFrench-language works237,207