Effect of dose intensification (DI) of octreotide-LAR (O-LAR) among symptomatic patients with neuroendocrine tumors (NETs).
Bibliographic record
Abstract
299 Background: O-LAR 20 to 30 mg IM monthly (qM) is approved for the management of symptomatic NETs. While higher doses are sometimes used for improved symptom control, the benefit of this is not well described. The objectives of the study were to evaluate pre-DI and post-DI symptom, biomarker and tumor size among patients who received O-LAR 40-60 mg qM. Methods: With approval of the BC Cancer Agency research ethics committee the charts of all referred patients with NETs who received O-LAR 40-60 mg qM between 2005 and 2001 were reviewed. Symptom severity was graded on a 4 point scale and any post-increase improvement from grade 2,3 or 4 to 1 or no symptoms was classified an improvement. Pre-DI Chromogranin A (CGA) and 24-hour urine 5-HIAA were compared with the median of 3 post-DI levels and a 10% decrease was classified as a decrease. Results: A total of 37 patients received DI therapy with 40 mg (36), 50 mg (3), and 60 mg (16), for a total of 55 DI events. Median age was 60 and 49, 19, 32% had a tumor of small bowel, pancreas, other, respectively. Post-DI CGA and 5 HIAA levels decreased in 31% (15/49) and 23 % of patients (8/35) respectively. Symptom improvement post DI was observed in 62% (13/21) with diarrhea, 76% (13/17) with flushing, 53% (8/15) with abdominal pain. Post DI, no decreases in tumor size were observed, 29% (14/49) had radiological stable disease and the remainder had progressive tumors. Conclusions: O-LAR 40-60 mg qM was associated with improved symptom control among NET patients with refractory secretory symptoms. CGA and 5-HIAA levels varied in response to DI and were not accurate indicators of symptom control. There was no evidence of tumor regression with O-LAR DI. [Table: see text]
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".