Using pet-ct to Reduce Futile Thoracotomy Rates in Non-Small-Cell Lung Cancer: A Population-Based Review
Bibliographic record
Abstract
BACKGROUND: Combined positron-emission tomography and computed tomography (pet-ct) reduces futile thoracotomy (ft) rates in patients with non-small-cell lung cancer (nsclc). We sought to identify preoperative risk factors for ft in patients staged with pet-ct. METHODS: We retrospectively reviewed all patients referred to the BC Cancer Agency during 2009-2010 who underwent pet-ct and thoracotomy for nsclc. Patients with clinical N2 disease were excluded. An ft was defined as any of a benign lesion; an exploratory thoracotomy; pathologic N2 or N3, stage iiib or iv, or inoperable T3 or T4 disease; and recurrence or death within 1 year of surgery. RESULTS: Of the 108 patients who met the inclusion criteria, ft occurred in 27. The main reason for ft was recurrence within 1 year (14 patients) and pathologic N2 disease (10 patients). On multivariate analysis, an Eastern Cooperative Oncology Group performance status greater than 1, a pet-ct positive N1 status, a primary tumour larger than 3 cm, and a period of more than 16 weeks from pet-ct to surgery were associated with ft. N2 disease that had been negative on pet-ct occurred in 21% of patients with a pet-ct positive N1 status and in 20% of patients with tumours larger than 3 cm and non-biopsy mediastinal staging only. The combination of pet-ct positive N1 status and a primary larger than 3 cm had 85% specificity, and the presence of either risk factor had 100% sensitivity, for ft attributable to N2 disease. CONCLUSIONS: To reduce ft attributable to N2 disease, tissue biopsy for mediastinal staging should be considered for patients with pet-ct positive N1 status and with tumours larger than 3 cm even with a pet-ct negative mediastinum.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".