Factors Influencing Treatment Selection and Survival in Advanced Lung Cancer
Bibliographic record
Abstract
Purpose: Despite numerous breakthrough therapies, inoperable lung cancer still places a heavy burden on patients who might not be candidates for chemotherapy. To identify potential candidates for the newly emerging immunotherapy-based treatment paradigms, we explored the clinical and biologic factors affecting treatment decisions. Methods: We retrospectively reviewed the records of patients diagnosed at our university-affiliated cancer centre between 1 January 2011 and 31 December 2013. Patient demographics, systemic treatment, and survival were examined. Results: During the 3-year study period, 683 patients fitting the inclusion criteria were identified. First-line therapy was administered in 49.5% of patients; only 22.4% received further lines of therapy. The main reasons for withholding therapy were poor performance status [ps (43.2%)], rapidly deteriorating ps (31.9%), patient refusal of therapy (20.9%), and associated comorbidities (4%). Older age, the presence of brain metastasis at diagnosis, and non-small-cell histology were also associated with therapeutic restraint. Oncology referrals were infrequent in patients who did not receive therapy (32.2%). Older patients and those with a poor ps experienced superior survival when treatment was administered (hazard ratio: 0.25; 95% confidence interval: 0.16 to 0.38; and hazard ratio: 0.44; 95% confidence interval: 0.23 to 0.87 respectively; p < 0.001). Conclusions: Advanced lung cancer still poses a therapeutic challenge, with a high proportion of patients being deemed unfit for therapy. This issue cannot be resolved until appropriate measures are taken to ensure the inclusion of older patients and those with a relatively poor ps in large clinical trials. Immunotherapy might be interesting in this setting, given that it appears to be more tolerable. Another consequential undertaking would be the deployment of strategies to reduce wait times during the diagnostic process for patients with a high index of suspicion for lung cancer.
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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".