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Record W2113687724 · doi:10.2217/cer.15.34

Impact of paliperidone palmitate versus oral atypical antipsychotics on healthcare outcomes in schizophrenia patients

2015· article· en· W2113687724 on OpenAlexaff
Yongling Xiao, Erik Muser, Marie‐Hélène Lafeuille, Jacqueline Pesa, John Fastenau, Mei Sheng Duh, Patrick Lefèbvre

Bibliographic record

VenueJournal of Comparative Effectiveness Research · 2015
Typearticle
Languageen
FieldMedicine
TopicSchizophrenia research and treatment
Canadian institutionsGroup for Research in Decision Analysis
Fundersnot available
KeywordsPaliperidone PalmitateMedicineSchizophrenia (object-oriented programming)MedicaidHealth carePaliperidoneFormularyMarginal structural modelPharmacyHealth economicsPsychiatryEmergency medicineAntipsychoticInternal medicineFamily medicineConfoundingPublic health

Abstract

fetched live from OpenAlex

AIM: To assess impact of initial treatment and time-dependent treatment with paliperidone palmitate (PP) versus oral atypical antipsychotics (OAAs) on healthcare resource utilization and costs. PATIENTS & METHODS: A retrospective longitudinal study was conducted among Medicaid beneficiaries with schizophrenia. Inverse probability treatment weighting method and marginal structural models were used to estimate the impact of treatment on healthcare resource utilization and costs, respectively. RESULTS: Compared to OAAs, PP was associated with lower medical costs (mean monthly cost difference [MMCD] = -US$256; p = 0.008), which offset the higher pharmacy expense (MMCD = US$122; p < 0.001) resulting in nonsignificant cost savings associated with PP (MMCD = -US$91; p = 0.689). CONCLUSION: PP was associated with comparable overall costs to OAAs, but with significantly lower medical costs, particularly attributable to reduced inpatient visits and long-term care admissions.

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)0.002
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 score0.005
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.282
GPT teacher head0.533
Teacher spread0.251 · 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

Citations26
Published2015
Admission routes1
Has abstractyes

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