Is lung stereotactic ablative radiotherapy safe after pneumonectomy?—a systematic review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Patients treated with surgery for lung cancer are at risk of second primary lung cancers (SPLCs), which when localized, may be amenable to radical treatment. Treatment options, however, are limited due to reduced cardiopulmonary reserve and competing mortality risks. The aim of this study was to perform a systematic review of publications examining treatment planning considerations, clinical outcomes, and toxicity rates of stereotactic ablative radiotherapy (SABR) in patients who have previously undergone pneumonectomy. A systematic review of the literature was conducted in accordance with PRISMA guidelines using PubMed and EMBASE from inception to July 2018. Articles were limited to those published in the English language. Non-review articles with patients who received exclusively lung SABR post-pneumonectomy were included. Two reviewers independently performed abstract and full-text review, with discrepancies settled by a third reviewer. Of the 215 articles identified by the initial search, 6 articles comprising 53 patients who received lung SABR post-pneumonectomy met inclusion criteria. The mean age was 68, and most patients were male (73.7%). The mean time to pneumonectomy was 6.5 years. The mean biologically effective dose was 115 Gy, and the most common dose fractionation schemes were 54 Gy in 3 fractions, 48 Gy in 4 fractions, and 50 Gy in 5 fractions. The mean follow-up was 25.4 months. The mean 1-year overall survival and 2-year local control rates were 80.6% and 89.4%. Grade 3 or higher toxicity was reported in 13.2% of patients. SABR appears to be a safe and feasible option for SPLCs in patients with prior pneumonectomy. Multi-institutional and/or prospective studies would be helpful to determine the true risk and appropriateness of SABR in this high-risk patient population.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 it