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Record W2587649778 · doi:10.1377/hlthaff.2016.0683

Longer Periods Of Hospice Service Associated With Lower End-Of-Life Spending In Regions With High Expenditures

2017· article· en· W2587649778 on OpenAlexaff
Shi‐Yi Wang, Sylvia H. Hsu, Siwan Huang, Pamela R. Soulos, Cary P. Gross

Bibliographic record

VenueHealth Affairs · 2017
Typearticle
Languageen
FieldMedicine
TopicPalliative Care and End-of-Life Issues
Canadian institutionsYork University
FundersNational Cancer InstituteAgency for Healthcare Research and Quality
KeywordsService (business)ReferralMedicineGeographic variationDemographyVariation (astronomy)GerontologyEnd-of-life careBusinessEnvironmental healthPalliative careNursing

Abstract

fetched live from OpenAlex

Hospice use is expected to decrease end-of-life expenditures, yet evidence for its financial impact remains inconclusive. One potential explanation is that the use of hospice may produce differential cost-savings effects by region because of geographic variation in end-of-life spending patterns. We examined 103,745 elderly Medicare fee-for-service beneficiaries in the Surveillance, Epidemiology, and End Results Program Medicare database who died from cancer in 2004-11. We created quintiles by the adjusted mean end-of-life expenditures per hospital referral region (HRR), and we examined HRR-level variation in the association between length of hospice service and expenditures across quintiles. Longer periods of hospice service were associated with decreased end-of-life expenditures for patients residing in regions with high average expenditures but not for those in regions with low average expenditures. Hospice use accounted for 8 percent of the expenditure variation between the highest and the lowest spending quintiles, which demonstrates the powers and limitations of hospice use for saving on costs.

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)0.000
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 score0.029
Threshold uncertainty score0.792

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.099
GPT teacher head0.396
Teacher spread0.297 · 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

Citations13
Published2017
Admission routes1
Has abstractyes

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