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Record W2129418752 · doi:10.1377/hlthaff.2009.0563

How Medicare’s Payment Cuts For Cancer Chemotherapy Drugs Changed Patterns Of Treatment

2010· article· en· W2129418752 on OpenAlexaff
Mireille Jacobson, Craig C. Earle, Mary Price, Joseph P. Newhouse

Bibliographic record

VenueHealth Affairs · 2010
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic and Financial Impacts of Cancer
Canadian institutionsCancer Care OntarioOntario Institute for Cancer Research
Fundersnot available
KeywordsMedicineChemotherapyCarboplatinMedical prescriptionDocetaxelPrescription drugPaclitaxelLung cancerPaymentDrugOncologyIntensive care medicineInternal medicineFinanceBusinessCisplatinPharmacology

Abstract

fetched live from OpenAlex

The Medicare Prescription Drug, Improvement, and Modernization Act, enacted in 2003, substantially reduced payment rates for chemotherapy drugs administered on an outpatient basis starting in January 2005. We assessed how these reductions affected the likelihood and setting of chemotherapy treatment for Medicare beneficiaries with newly diagnosed lung cancer, as well as the types of agents they received. Contrary to concerns about access, we found that the changes actually increased the likelihood that lung cancer patients received chemotherapy. The type of chemotherapy agents administered also changed. Physicians switched from dispensing the drugs that experienced the largest cuts in profitability, carboplatin and paclitaxel, to other high-margin drugs, like docetaxel. We do not know what the effect was on cancer patients, but these changes may have offset some of the savings projected from passage of the legislation. The ultimate message is that payment reforms have real consequences and should be undertaken with caution.

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)0.005
metaresearch head score (Gemma)0.031
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 score0.035
Threshold uncertainty score0.069

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.031
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0030.002
Open science0.0000.001
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 head0.032
GPT teacher head0.282
Teacher spread0.250 · 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

Citations179
Published2010
Admission routes1
Has abstractyes

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