Performance-Status Deterioration during Sequential Chemo-Radiotherapy as a Predictive Factor in Locally Advanced Non-Small Cell Lung Cancer
Bibliographic record
Abstract
The role of sequential chemoradiotherapy in non-small cell lung cancer (NSCLC) patients who are not eligible for concurrent therapy has not been clearly defined. The aim of this study was to determine the usefulness of Karnofsky performance status (KPS) monitoring and to define the factors determining clinical deterioration during sequential chemoradiotherapy in patients treated from July 2009 to October 2014. The study included 196 patients. The clinical stage was defined as III A in 94 patients (48%) and III B in 102 patients (52%). Reduced KPS was found in 129 patients (65.8%). Baseline KPS had no significant prognostic significance. Deterioration of KPS during chemoradiotherapy was observed in 53 patients (27%) and had a negative predictive value for both worse-progression free survival (HR = 1.44; 95% CI: 1.03–1.99; p = 0.03) and overall survival (HR = 1.42; 95% CI: 1.02–1, 99; p = 0.04). The deterioration of KPS correlated with the disease control rate 6 weeks after the end of chemoradiotherapy (p = 0.0085). The risk of KPS worsening increased with each subsequent day between the end of chemotherapy and the start of radiotherapy (OR = 1.03; 95%CI: 1.01–1.05; p = 0.001), but decreased with each year of older age of patients (OR = 0.94, 95% CI: 0.9–0.98, p = 0.009). The time between the end of chemotherapy and the start of radiotherapy determined the prognosis of NSCLC after chemoradiotherapy. It should be adjusted to the age of patients.
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.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.000 | 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 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".