Diagnostic Patterns of Non-Small-Cell Lung Cancer at Princess Margaret Cancer Centre
Bibliographic record
Abstract
Background: Accurate classification of lung cancer subtypes has become critical in tailoring lung cancer treatment. Our study aimed to evaluate changes in diagnostic testing and pathologic subtyping of advanced non-small-cell lung cancer (NSCLC) over time at a major cancer centre. Methods: In a review of patients diagnosed with advanced NSCLC at Princess Margaret Cancer Centre between 2007–2009 and 2013–2015, diagnostic method, sample type and site, pathologic subtype, and use of immunohistochemistry (IHC) staining and molecular testing were abstracted. Results: The review identified 238 patients in 2007–2009 and 283 patients in 2013–2015. Over time, the proportion of patients diagnosed with adenocarcinoma increased to 73.1% from 60.9%, and diagnoses of NSCLC not otherwise specified (NOS) decreased to 6.4% from 18.9%, p < 0.0001. Use of diagnostic bronchoscopy decreased (26.9% vs. 18.4%), and mediastinal sampling procedures, including endobronchial ultrasonography, increased (9.2% vs. 20.5%, p = 0.0001). Use of IHC increased over time to 76.3% from 41.6% (p < 0.0001). Larger surgical or core biopsy samples and those for which IHC was performed were more likely to undergo biomarker testing (both p < 0.01). Conclusions: Customizing treatment based on pathologic subtype and molecular genotype has become key in treating patients with advanced lung cancer. Greater accuracy of pathology diagnosis is being achieved, including through the routine use of IHC.
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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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".