Evaluating Diagnostic and Treatment Timelines for ALK-Positive NSCLC Patients: Results from a Global Registry for Consideration in the Journal of Clinical Lung Cancer
Bibliographic record
Abstract
ALK-positive non-small cell lung cancer (NSCLC) is an aggressive subtype affecting younger, nonsmoking individuals. Tyrosine kinase inhibitors (TKIs) are essential treatments; however, global data on comorbidities, diagnosis, treatment, and intervals from initial medical visits to TKI treatment remain limited. This study examines symptoms, comorbidities, and treatment timelines to identify factors contributing to delays in TKI treatment. We conducted a longitudinal observational study using a global registry of ALK-positive NSCLC patients via online surveys from September 2022 to November 2024. Participants were recruited through support groups, patient newsletters, and oncologist referrals. Descriptive statistics and multivariable regression analyses evaluated the impact of sociodemographics, comorbidities, symptoms, and clinical practices on diagnostic and treatment intervals. Surveys from 1,288 individuals across 71 countries revealed a median diagnosis age of 52 years, with 52% residing in the U.S. and 28% reporting a smoking history. The median time from the first medical visit to diagnosis was 45 days. For stage IIIC/IV patients, the median time from diagnosis to TKI treatment was 30 days. Older age at the first visit was linked to shorter diagnostic intervals, while more symptoms increased delays. Older age, GERD, and high blood pressure prolonged treatment intervals among advanced-stage patients, whereas coughing up blood and voice changes shortened them. Additionally, recent diagnosis and residence in Canada were associated with shorter treatment delays. This study underscores disparities in diagnosis and treatment timelines for ALK-positive NSCLC and highlights the need for targeted interventions to optimize care and improve outcomes.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".