Bibliographic record
Abstract
Outcomes in patients with follicular lymphoma (FL) have improved dramatically over the last decade. However, novel agents are greatly needed for those who exhibit treatment resistance, in order to minimize lifelong toxicity and to enable combinations that may allow us to achieve the elusive goal of cure. Biological advances have led to the discovery of a large number of potential therapeutic targets and the development of a plethora of novel agents designed to exploit these processes. Possible targets include tumor cell surface markers, key components of intracellular pathways and epigenetic mechanisms, and reactive cells of the microenvironment. Given the large number of candidate drugs and potential combinations, it will be crucial to prioritize evaluation based on sound preclinical and early clinical studies. Combinations that exploit driver mechanisms within tumor cells and target parallel pathways to minimize the development of drug resistance, as well as harness the potential of the immune system would seem most logical. In order to expedite progress, future studies will need to use innovative trial designs and employ surrogate end points. The development of validated prognostic tools to identify higher risk patients and reliable predictive markers to select subgroups most likely to benefit from targeted agents will be paramount. The potential for unexpected toxicity with novel combinations must be recognized, necessitating both short- and long-term vigilance. Finally, as a greater number of treatment options become available, optimal sequencing must be determined in order to both prolong life and maintain its quality.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".