Order of treatment with ALK inhibitors and its effect on people with lung cancer in the real world: a plain language summary
Bibliographic record
Abstract
Plain Language SummaryWhat is this summary about?This is a summary of the results of a recent study. Researchers studied people with ALK-positive advanced non-small cell lung cancer (NSCLC) who were treated with medicines called ALK inhibitors. In the study, researchers looked at the order (or sequence) these medicines were given to people in the real world (not part of a clinical study), and how long the people took each medicine when they were given it as a first or second treatment. Several different ALK inhibitors are approved to treat this type of cancer. Understanding how well diffeent treatment sequences work will help healthcare professionals select the right treatments for their patients.What did this study find?This study used de-identified medical information from 273 patients in the Flatiron database (a collection of information about patients with cancer). This study found that in people who stopped their first ALK inhibitor treatment, about 1 out of 5 (22%) died without receiving a second treatment, and at least half of these people died within 4.0 months of stopping the first treatment. At least half of the people stopped taking their first treatment after 21.9 months and their second treatment after 7.3 months. At least half of the people stopped taking their first and second treatments after 29.4 months. When ALK inhibitors were taken in any sequence during treatment, at least half of the people stopped taking ALK inhibitors after 28.0 months.What do the findings of this study mean?The findings from this study show that not all people who stop taking their first treatment are able to take a second treatment. People take their first treatment for a longer time than their second treatment, highlighting the importance of selecting an effective first treatment option.This is an abstract of the Plain Language Summary of Publication article.View the full Plain Language Summary PDF of this article to read the full-text
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".