Canadian Consensus: Oligoprogressive, Pseudoprogressive, and Oligometastatic Non-Small-Cell Lung Cancer
Bibliographic record
Abstract
Background: Little evidence has been generated for how best to manage patients with non-small-cell lung cancer (nsclc) presenting with rarer clinical scenarios, including oligometastases, oligoprogression, and pseudoprogression. In each of those scenarios, oncologists have to consider how best to balance efficacy with quality of life, while maximizing the duration of each line of therapy and ensuring that patients are still eligible for later options, including clinical trial enrolment. Methods: An expert panel was convened to define the clinical questions. Using case-based presentations, consensus practice recommendations for each clinical scenario were generated through focused, evidence-based discussions. Results: Treatment strategies and best-practice or consensus recommendations are presented, with areas of consensus and areas of uncertainty identified. Conclusions: In each situation, treatment has to be tailored to suit the individual patient, but with the intent of extending and maximizing the use of each line of treatment, while keeping treatment options in reserve for later lines of therapy. Patient participation in clinical trials examining these issues should be encouraged.
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 | 0.048 | 0.097 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.012 | 0.011 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".