Analyzing the Effect of Physician Assignment in the Survival of Patients with Advanced Non-Small-Cell Lung Cancer
Bibliographic record
Abstract
Background: Non-small-cell lung cancer (nsclc) is the most common cause of cancer deaths worldwide, with a 5-year survival of 17%. The low survival rate observed in patients with nsclc is primarily attributable to advanced stage of disease at diagnosis, with more than 50% of cases being stage iv at presentation. For patients with advanced disease, palliative systemic therapy can improve overall survival (os); however, a recent review at our institution of more than 500 consecutive cases of advanced nsclc demonstrated that only 55% of the patients received palliative systemic therapy. What is unknown to date is whether that observed low rate of systemic therapy in our previous study is uniform across oncologists. Methods: With ethics approval, we performed a retrospective analysis of newly diagnosed patients with stage iv nsclc seen as outpatients at our institution between 2009 and 2012 by 4 different oncologists. Demographics, treatment, and survival data were collected and compared for the 4 oncologists. Results: The 4 oncologists saw 528 patients overall, with D seeing 115; L, 158; R, 137; and M, 118. Significant variation was observed in the proportion receiving 1 line or more of chemotherapy: D, 60%; L, 65%; R, 43%; and M, 52%. Physician assignment was not associated with a difference in median os, with D's cohort having a median os of 6.8 months; L, 8.4 months; R, 7.0 months; and M, 7.0 months. Conclusions: Practice size and proportion of patients treated varied between oncologists, but those differences did not translate into significantly different survival outcomes for patients.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".