Clinical impact of unsuccessful subcutaneous administration of octreotide <scp>LAR</scp> instead of intramuscular administration in patients with metastatic gastroenteropancreatic neuroendocrine tumors
Bibliographic record
Abstract
Octreotide LAR is a long-acting somatostatin analogue (SSA) used in the management of metastatic gastroenteropancreatic neuroendocrine tumors (GEP NETs). It requires intramuscular (IM) injection. Missed IM injections cause subcutaneous nodules (SCNs) on radiologic images. We reviewed the rates of SCNs in a real-world cohort of GEP NETs receiving octreotide LAR and explored treatment outcomes. Patients commencing octreotide LAR between August 5, 2010 and March 8, 2018 at a single cancer center in Canada were identified from pharmacy records. Patients were included if they had a computed tomography (CT) scan performed at the time of progression and a preceding CT with pelvis included to enable assessment for the presence of nodules. Fisher's exact test was used to examine predictors of SCNs, and Kaplan-Meier curves summarized differences in progression free (PFS) and overall survival (OS) that were compared with log-rank tests. Of 243 patients receiving octreotide LAR, 45 had all required CT images available for central review. SCNs were found in 20/45 (44%) of patients on the last scan showing stable disease before progression and were numerically but not statistically more likely in females (OR: 2.36, 95% CI: 0.66-8.29, p = .23). There was an increased risk of SCNs in patients with a skin-to-muscle distance >38 mm (the length of an octreotide LAR needle) on CT (OR: 5.09, 95% CI: 1.39-16.6, p = .018) and a trend toward increased risk in obese patients (OR: 5.71, 95% CI: 1.26-23.4, p = .061). PFS (HR: 1.01, 95% CI: 0.56-1.78, p = .98) and OS (HR: 0.86, 95% CI: 0.41-1.8, p = .70) was similar between those with/without SCNs. In conclusion, almost half of patients receiving octreotide LAR had SCNs; however, missed administration of SSA did not appear to result in worse survival in this small study. Factors such as sex, younger age skin-to-muscle distance, and obesity may affect SCN development and should be considered when choosing an SSA.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".