Patterns of use of 18-fluoro-2-deoxy-D-glucose positron emission tomography for initial staging of grade 1–2 follicular lymphoma and its impact on initial treatment strategy in the National Comprehensive Cancer Network Non-Hodgkin Lymphoma Outcomes database
Bibliographic record
Abstract
We describe the patterns of use of 18-fluoro-2-deoxy-D-glucose positron emission tomography (FDG-PET) for the initial staging of patients with newly diagnosed grade 1-2 follicular lymphoma (FL) and its potential impact on treatment. Data were obtained from the National Comprehensive Cancer Network Non-Hodgkin Lymphoma Outcomes database. Patients who presented between 1 January 2001 and 30 September 2009 with newly diagnosed grade 1-2 FL, with at least 6 months of follow-up, were included. We identified 953 eligible patients and 532 (56%) underwent FDG-PET as part of initial staging. Among patients who underwent FDG-PET for initial staging, 438 (82%) received early treatment compared to 259 (61.5%) of those staged without FDG-PET (p < 0.0001). Of all patients with stage I FL (n = 100), 47% were treated with radiotherapy (RT) alone, and the choice of initial treatment strategy for stage I FL did not vary significantly by use of FDG-PET (p = 0.22). The use of FDG-PET for staging of FL is widespread and is associated with a greater proportion of patients receiving early therapy. Given the widespread use and high cost of FDG-PET, its clinical utility in stage I FL should be further evaluated.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".