Are the data on quality of life and patient reported outcomes from clinical trials of metastatic non-small-cell lung cancer important?
Bibliographic record
Abstract
Majority of the patients with advanced non-small-cell lung cancer (NSCLC) experience two or more disease related symptoms, which may have a negative impact on their health-related quality of life (HR QOL). These patients prefer a therapy that would improve disease related symptoms, as opposed or treatment that slightly prolongs their survival without improving symptoms. The improvements of the symptoms augment the significance of improved response rates or progression free survivals. The choice of the questionnaires to evaluate patients-reported outcomes (PROs) and HRQOL benefits and methods of collecting the data and their interpretations are very important and are discussed in this manuscript. PROs and HR QOL outcomes are important in patients with advanced NSCLC only when the data are collected and analyzed correctly. Then they can be viewed as components of the total value of a treatment, providing a comprehensive picture of the benefits and risks of anticancer therapies. Enabling the patients to feel during the last months of their lives more comfortable and not be dependent on their loved ones is a very important task in the treatment of advanced NSCLC.
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 | 0.201 | 0.425 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.006 | 0.013 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".