Forecasting the impact of stereotactic ablative radiotherapy for early-stage lung cancer on the thoracic surgery workforce
Bibliographic record
Abstract
OBJECTIVES: To predict variation in thoracic surgery workforce requirements with the introduction of stereotactic ablative radiotherapy (SABR) for the treatment of early-stage non-small-cell lung cancer (NSCLC). METHODS: Using Canadian census microdata and the Canadian Community Health Survey, a microsimulation model representing the national population was developed. The demand component simulates the incidence of lung cancer, incorporating the impact of computed tomography (CT) screening for high-risk individuals (>30 pack-year smoking history; age 55-74 years). The supply component simulates the number of thoracic surgeons. SABR was introduced into the model to predict changes in the number of operable NSCLC cases per thoracic surgeon, modelling 30, 60 and 90% compliance with SABR for Stage IA and then for both Stage IA and IB NSCLC. RESULTS: In the absence of SABR, the volume of operative NSCLC per surgeon increases by a peak of 49.4% (by 2027) and then gradually declines to the present day volume by 2049. More dramatic decreases are seen with increasing compliance with SABR for Stage IA/IB NSCLCs. If the number of new surgeons entering the workforce per year were reduced by 33%, the operative volume per surgeon would increase by a peak of 57.1% (30% Stage IA SABR compliance) and would decrease by up to 49.1% (90% Stage IA SABR compliance). CONCLUSIONS: With the implementation of SABR for treatment of early NSCLC, there would be a decrease in operative volume. The impact would depend on the stage of NSCLC for which SABR is recommended and on compliance. A national strategy for thoracic surgery workforce planning is necessary, given the complex interaction of CT screening and the treatment of medically operable early NSCLC with SABR.
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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 | 0.008 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| 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".