Real world use of lanreotide in neuroendocrine tumors
Bibliographic record
Abstract
Background: Treatment for metastatic neuroendocrine tumors (NETs) is often with somatostatin analogues (SSA) such as lanreotide in the first-line setting. Real world use of lanreotide in Canada is not well studied. Methods: We performed a retrospective chart review of 69 patients to study real world use of lanreotide at our centre. Results: Lanreotide was the first-line of systemic treatment in 60 patients. Watch-and-wait was a common strategy and was seen in 31 patients. SSA switch strategy was seldom applied. Majority of patients on lanreotide had low-grade NETs. Standard starting dose of lanreotide 120 mg every 28 days was used in 66 patients. Dose escalation to 120 mg every 21 days occurred in 7 patients. The primary intention for treatment was tumor control in 32 patients, and both tumor and symptom control in 34 patients. Median time on treatment was 21.6 months. Conclusions: Overall, our findings were in keeping with current guidelines. It will be interesting to assess how clinical practice evolves in the future and to determine the role of dose escalation for disease control.
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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".