MétaCan
Menu
Back to cohort
Record W102546209 · doi:10.1177/082585970602200103

Indicators of Poor Quality End-of-Life Cancer Care in Ontario

2006· article· en· W102546209 on OpenAlexafffundabout
Lisa Barbera, Lawrence Paszat, Carole Charter

Bibliographic record

VenueJournal of Palliative Care · 2006
Typearticle
Languageen
FieldMedicine
TopicPalliative Care and End-of-Life Issues
Canadian institutionsCancer Care OntarioInstitute for Clinical Evaluative SciencesUniversity of Toronto
FundersOntario Ministry of Health and Long-Term Care
KeywordsMedicinePalliative careCohortEnd-of-life careCancerQuality of life (healthcare)Intensive care unitEmergency medicineOddsCancer registryCohort studyHealth careOdds ratioFamily medicineGerontologyIntensive care medicineLogistic regressionNursingInternal medicine

Abstract

fetched live from OpenAlex

This study measures the proportion of cancer patients in Ontario, Canada, with intensive care unit (ICU) admissions, emergency room (ER) visits, or chemotherapy in the last two weeks of life. We used the Ontario Cancer Registry to identify a cohort of cancer patients who died in 2001. These cases were then linked to administrative sources of data to measure each indicator, and to describe the associated clinical and health service factors. In the cohort, 27% had at least one ER visit and 5% had an ICU visit in the last two weeks of life. Of those who received chemotherapy in the last six months, 16% received chemotherapy in the last two weeks of life. Receiving a home care visit in the last six months of life, or a physician house call or a palliative care assessment in the last two weeks of life was consistently associated with decreased odds of each of the indicators. Our results indicate that a significant proportion of Ontario cancer patients have indicators of poor quality end-of-life care. Certain health care factors may influence these indicators.

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 score0.036
Threshold uncertainty score0.111

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.000
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 head0.102
GPT teacher head0.431
Teacher spread0.329 · 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

Citations228
Published2006
Admission routes3
Has abstractyes

Explore more

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