Real world outcome analysis of the two McGill University–associated hospitals with Lu-177 PRRT for metastatic NET.
Bibliographic record
Abstract
e16347 Background: Neuroendocrine tumours (NET) are a heterogenous group of neoplasms that arise from neuroendocrine cells within various organs. Their aggressivity and metastatic potential varies vastly based on the anatomic site of origin and the histopathological grade. Gastroenteropancreatic neuroendocrine tumours (GEP-NET) are the most common NETs accounting for 55-70% of all diagnosed NETs. Despite the benefits in PFS, response rates and likely OS as a result of the use of Lutathera, its implementation in wider clinical practice outside the established PRRT centres in Europe and the US has been rather slow due to difficulties with acquisition of Dotatate imaging and subsequent treatment, as only certain specialized centres have experience and access to the required resources. At McGill, both the MUHC and the Jewish General Hospital offer PRRT for GEP-NETs. The aim of this retrospective cohort study is to investigate local institutional practices in relation to 177-Lu DOTATATE PRRT in GEP-NETs. Real world data from the MUHC and the JGH will be analyzed in order determine treatment responses and associated sequencing of treatment. Methods: This retrospective cohort study aims to assess response rates in all patients treated with PRRT at the MUHC and the Jewish General Hospital since the initiation of the 177-Lu DOTATATE treatment program. The data was obtained through retrospective chart review and includes a cohort comprised of patients from both the Jewish General Hospital and the MUHC. Baseline demographics, as well as treatment-related demographics were obtained. Descriptive statistics were be calculated for the overall population using frequencies for categorical variables and means (SDs) or medians (interquartile ranges) for continuous and count variables. Survival/progression free survival estimates were computed using the Kaplan-Meier method. Potential association between variables was measured using Pearson correlation coefficients, chi-square tests, one- or two-sample t-tests.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".