Bibliographic record
Abstract
Follicular lymphoma (FL) is the most common indolent subtype of non-Hodgkin Lymphoma (NHL) and the second most common type of lymphoma overall. In Canada the age‑standardized incidence of FL is 38.3 cases per million individuals per year with mean age at diagnosis of 60 and similar incidence in men and women. Follicular lymphoma is treatable but not curable with systemic therapy yet it maintains a median overall survival (OS) of approximately 20 years. Historically, this long median survival has been maintained through periods of watchful waiting and subsequent treatment with chemoimmunotherapy when the disease burden becomes symptomatic. Serial relapses with progressively shorter remissions and more resistant disease is the usual natural history for FL. The management of relapsed FL remains controversial and the decision on next line of therapy is a rapidly evolving area, with the old standard repetition of chemoimmunotherapy being contested by new targeted therapies. There remains a challenge for Canadian patients to access these novel therapies outside of clinical trials and access programs. This review will present a treatment approach for relapsed FL taking into consideration Canadian funding patterns, in addition to reviewing the novel drugs with the highest level and most mature evidence to date.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".