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Record W4366464071 · doi:10.1097/mpa.0000000000002144

A Real-World Observational Study of the Use and Associated Costs of Treating Neuroendocrine Tumors With Somatostatin Analogs in Canada

2022· article· en· W4366464071 on OpenAlexaffabout
Winson Y. Cheung, Callahan LaForty, A Liovas, Heather McKechnie, Jonathan M. Loree

Bibliographic record

VenuePancreas · 2022
Typearticle
Languageen
FieldMedicine
TopicNeuroendocrine Tumor Research Advances
Canadian institutionsUniversity of Calgary
FundersIpsen
KeywordsLanreotideMedicineOctreotideConfidence intervalHazard ratioInternal medicineObservational studyDosingOdds ratioSomatostatinAcromegalyHormone

Abstract

fetched live from OpenAlex

OBJECTIVES: Somatostatin analogs (SSAs; lanreotide autogel and octreotide long-acting release) are used to treat neuroendocrine tumors; however, factors that influence SSA use are unclear. METHODS: This real-world, observational study collected data from private/public pharmacy claims for patients using SSAs in Canada. Data relating to dosing regimens, injection burden, treatment persistence, and costs were retrospectively analyzed for treatment-naive patients. RESULTS: Overall, 1545 patients were included in the analysis of dosing regimens, 908 for injection burden, 453 for treatment persistence, and 903 for treatment-associated costs. Compared with lanreotide, treatment with octreotide long-acting release was more likely associated with treatment above the maximum recommended dose (odds ratio, 16.2; 95% confidence interval, 4.3-136.2; P < 0.0001), higher weighted average long-acting SSA injection burden (13.4 vs 12.5, P < 0.0001), and a higher number of rescue medication claims per patient (0.22 vs 0.03, P < 0.0001). Treatment with lanreotide autogel was associated with greater treatment persistence (hazard ratio, 0.58; 95% confidence interval, 0.42-0.80; P = 0.001) and lower mean annual costs of treatment than octreotide long-acting release (Canadian dollars $27,829.35 vs $31,255.49; P < 0.0001). CONCLUSIONS: These findings provide valuable insight into SSA use in clinical settings and may inform treatment selection.

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)0.001
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 score0.026
Threshold uncertainty score0.331

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.058
GPT teacher head0.306
Teacher spread0.247 · 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

Citations1
Published2022
Admission routes2
Has abstractyes

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