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Record W2897088769 · doi:10.1136/lupus-2018-lsm.42

CS-07 Economic evaluation of damage accrual in an international SLE inception cohort

2018· article· zh· W2897088769 on OpenAlexaffabout
Ann E. Clarke, Ian N Bruce, Murray B. Urowitz, John G. Hanly, Juanita Romero‐Díaz, Caroline Gordon, Sang‐Cheol Bae, Sasha Bernatsky, Daniel J. Wallace, Joan T. Merrill, David Isenberg, Anisur Rahman, Ellen M. Ginzler, Michelle Petri, MA Dooley, Paul R. Fortin, Dafna D. Gladman, Jorge Sánchez‐Guerrero, Kristján Steinsson, Rosalind Ramsey‐Goldman, Munther A. Khamashta, Cynthia Aranow, Graciela S. Alarcón, Susan Manzi, Ola Nived, Asad Zoma, Ronald van Vollenhoven, Manuel Ramos‐Casals, Guillermo Ruiz‐Irastorza, S Sam Lim, Kenneth Kalunian, Murat İnanç, Diane L. Kamen, Christine Peschken, Søren Jacobsen, Anca Askanase, Yvan St. Pierre, Li Su, Vernon T. Farewell

Bibliographic record

Venuenot available
Typearticle
Languagezh
FieldEconomics, Econometrics and Finance
TopicHealth Systems, Economic Evaluations, Quality of Life
Canadian institutionsMcGill University Health CentreUniversity of TorontoDalhousie UniversityUniversity of ManitobaCentre hospitalier universitaire de QuébecToronto Western HospitalUniversité LavalQueen Elizabeth II Health Sciences CentreUniversity of Calgary
Fundersnot available
KeywordsMedicineCohortAccrualDemographicsDemographyHealth careEthnic groupInternal medicineEmergency medicinePediatricsFinance

Abstract

fetched live from OpenAlex

Background Little is known about the association of healthcare costs with damage accrual in SLE. We describe the costs associated with damage progression using multi-state modeling. Methods Patients fulfilling the revised ACR Classification Criteria for SLE from 32 centres in 11 countries were enrolled in the Systemic Lupus International Collaborating Clinics (SLICC) inception cohort within 15 months of diagnosis. Annual data on demographics, SLE disease activity (SLEDAI-2K), damage (SLICC/ACR Damage Index [SDI] if ≥6 months from diagnosis), hospitalizations, medications, dialysis, and utilization of selected medical/surgical procedures were collected. Annual health resource utilization was costed using 2017 Canadian prices. Annual costs associated with SDI states were obtained from multiple regressions adjusting for age, sex, race/ethnicity, and disease duration. As there were relatively few transitions to SDI states 5–11, these were merged into a single SDI state. Five and 10 year cumulative costs were estimated by multiplying annual costs associated with each SDI state by the expected duration in each state, which was forecasted using a multi-state model and longitudinal SDI data from the SLICC Inception Cohort (Bruce IN et al. Ann Rheum Dis 2015;74:1706–13). Future costs were discounted at a yearly rate of 3%. Results 1676 patients participated, 88.7% female, 49.2% Caucasian, mean age at diagnosis 34.6 years (SD 13.4), mean disease duration at enrollment 0.5 years (range 0–1.3 years), and mean follow up 7.8 years (range 0.6–16.9 years). Health resource utilization and annual costs (after adjustment using regression) were markedly higher in those with higher SDIs (SDI=0, annual costs $1847, 95% CI $1120 to $2574; SDI≥5, annual costs $26 772, 95% CI $19 631 to $33 813). At SDI≤2, hospitalizations and medications accounted for 97.1% of direct costs, whereas at SDI≥3, dialysis was responsible for 55.0%. Five and 10 year cumulative costs stratified by baseline SDI were calculated by multiplying the annual costs associated with each SDI by the expected duration in that state. Five and 10 year costs were greater in those with the highest SDIs at baseline (table 1). Conclusions Patients with the highest baseline SDIs incur annual costs and 10 year cumulative costs that are at least 10-fold higher than those with the lowest baseline SDI. By estimating the expected duration in each SDI state and incorporating annual costs, disease severity at presentation can be used to predict future healthcare costs, critical knowledge for cost-effectiveness evaluations of novel therapies. Acknowledgements The Systemic Lupus International Collaborating Clinics (SLICC) research network received partial funding for this study from UCB Pharmaceuticals.

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)0.012
metaresearch head score (Gemma)0.026
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 score0.065
Threshold uncertainty score0.130

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.026
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.004
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0060.001

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 head0.426
GPT teacher head0.504
Teacher spread0.078 · 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".

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Citations0
Published2018
Admission routes2
Has abstractyes

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