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Record W2068066595 · doi:10.2217/lmt.14.39

Home Care Utilization and Costs in Stage IV Lung Cancer: a Canadian Public Payer Experience

2014· article· en· W2068066595 on OpenAlexaffabout
Nicole Mittmann, Soo Jin Seung, Ning Liu, Joan Porter, Natasha B. Leighl, Maureen Trudeau, William K. Evans, Craig C. Earle

Bibliographic record

VenueLung Cancer Management · 2014
Typearticle
Languageen
FieldMedicine
TopicPalliative Care and End-of-Life Issues
Canadian institutionsMcMaster UniversityHealth Sciences CentrePrincess Margaret Cancer CentreUniversity Health NetworkUniversity of TorontoInstitute for Clinical Evaluative SciencesSunnybrook Health Science Centre
Fundersnot available
KeywordsMedicineCohortStage (stratigraphy)Lung cancerEmergency medicinePublic healthCancerDiseaseInternal medicineIntensive care medicinePathology

Abstract

fetched live from OpenAlex

SUMMARY Background: Lung cancer (LC) is a leading cause of morbidity and mortality and there is limited information on the type, quantity and cost of home care services (HCS) for LC. Aim: The objectives of this study include: identifying a stage IV LC cohort; determining the utilization and costs of HCS for the stage IV LC cohort; and comparing HCS utilization and costs by phase of disease. Methods: New cases of stage IV LC were extracted from a provincial cancer registry and linked to administrative datasets. HCS utilization and costs (2009 Canadian dollars [CAD]) for stage IV cases were determined from a public payer perspective and by disease phase. Results: There are 4616 stage IV LC patients who used HCS.. The mean number of HCS visits per 30 days was 7.7 and the mean cost per 30 days was CAD$798 for terminal-phase patients. Conclusion: HCS costs for stage IV patients are less expensive than other health resources.

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 score0.049
Threshold uncertainty score0.354

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.007
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.066
GPT teacher head0.405
Teacher spread0.339 · 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

Citations0
Published2014
Admission routes2
Has abstractyes

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