MétaCan
Menu
← Back to cohort

Days Spent at Home in the Last 6 Months of Life. a Potential Patient-Determined Quality Indicator for Patients with Hematologic Malignancies at the End of Life

2017· article· en· W3134147891 on OpenAlexaffabout
Matthew C. Cheung, Sarah K. Andersen, Craig C. Earle, Ruth Croxford, Simron Singh

Bibliographic record

VenueBlood · 2017
Typearticle
Languageen
FieldMedicine
TopicPalliative Care and End-of-Life Issues
Canadian institutionsHealth Sciences CentreOntario Institute for Cancer ResearchUniversity of TorontoInstitute for Clinical Evaluative SciencesSunnybrook Health Science Centre
Fundersnot available
KeywordsMedicineHematologic malignancyQuality of life (healthcare)CohortPopulationEmergency medicinePalliative careHematologic NeoplasmsLogistic regressionPsychological interventionCancerInternal medicine

Abstract

fetched live from OpenAlex

Abstract Despite advances in the management of patients with hematologic malignancies, a significant proportion of patients will still die of their disease. Current quality indicators at the end of life (EOL) focus on whether patients receive aggressive interventions, including chemotherapy in the last days of life. However, frail patients and caregivers themselves have recently highlighted that “time spent at home” represents the most important measure of EOL quality care (Groff et al., NEJM 2016). This potential patient-determined quality indicator has not been previously studied in patients with hematologic malignancies. We used population-based health system administrative databases from Ontario, Canada. We identified a cohort of all adult patients who died of a hematologic malignancy from January 2005 to December 2013 within the Ontario Cancer Registry. The primary outcome of “days at home” in the last 6 months of life was defined as: 180 days minus the number of days in an acute care facility (inpatient, emergency department, and same day surgery stays), an inpatient rehabilitation facility, skilled nursing facility, or chronic care facility. We further identified patient variables (including comorbidities identified by a mortality risk score by Austin and van Walraven, Med Care 2011) and system level variables (including palliative care consultation prior to the last 6 months of life) that predicted for number of days at home and identified trends over time. We used a logistic regression model to identify relevant predictors of time spent at home beyond the median. During the relevant time-period, 6792 patients with hematologic malignancies died in Ontario. Median age was 72 (IQR 62-80) at the time of cancer death and 58% of patients were male. Median comorbidity score, measured by the mortality risk score, was moderate-to-high at 27 (IQR 20-34), 15% of patients were rural-based, and 27% had received palliative care consultation prior to the last 6 months of life. The median number of days at home in the last 6 months of life was 166 days; 81% of patients spent more than 120 days at home over the last 6 months of life. Increased age (per five year increase OR 1.14; CI 1.12-1.17; p In a large population of patients who die from hematologic malignancies, >80% of patients spend >120 days at home in the last 6 months of life. Certain demographic features of patients more likely to die at home suggest an important role for access to caregiver support (i.e. spousal/family support). Importantly, earlier access to palliative care may increase the likelihood that patients can die at home, representing a modifiable/actionable factor for physicians to consider to improve the quality of care in patients who are nearing the end of life. Disclosures No relevant conflicts of interest to declare.

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)0.006
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 score0.484
Threshold uncertainty score0.963

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.003
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.059
GPT teacher head0.333
Teacher spread0.274 · 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

Citations1
Published2017
Admission routes2
Has abstractyes

Explore more

Same venueBlood→Same topicPalliative Care and End-of-Life Issues→French-language works237,207→