A Real-World Observational Study of the Use and Associated Costs of Treating Neuroendocrine Tumors With Somatostatin Analogs in Canada
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".