Utilization of prophylactic cranial irradiation in patients with limited stage small cell lung carcinoma
Bibliographic record
Abstract
BACKGROUND: This study reports the adoption of prophylactic cranial irradiation (PCI) in patients with limited stage small cell lung carcinoma (LS-SCLC) at Princess Margaret Hospital (PMH) and the factors that impact PCI utilization. METHODS: A retrospective review was performed on all patients with LS-SCLC treated at PMH from 1997 to 2007. Clinical details including the rate of PCI utilization were determined and, for patients not receiving PCI, the documented reason was recorded. Brain failure free survival (FFS) and overall survival (OS) were estimated by the Kaplan-Meier method, comparing patients treated with or without PCI. Pearson chi-square test was used to determine factors associated with PCI use. RESULTS: Two hundred seven patients were treated for LS-SCLC and 61.4% (n = 127) of these patients received PCI. The most common documented reason for not receiving PCI was patient refusal, typically because of concerns about PCI toxicity. Patients older than 65 were significantly less likely to receive PCI. Brain FFS and OS rates were significantly higher in patients who received PCI. CONCLUSIONS: Not all eligible patients are receiving PCI, despite its significant effect on reducing brain metastases and improving OS. Emphasizing the benefits of PCI to patients, when discussing potential toxicities, may improve utilization.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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".