Palliative Thoracic Radiotherapy for Non-Small Cell Lung Cancer in Outpatients: Reasons for Unplanned Hospitalization and Its Impact on Survival
Bibliographic record
Abstract
BACKGROUND: The aims of the study were to examine the rates of and reasons for unplanned hospitalization after start of palliative radiotherapy or chemoradiation (CRT), and to study whether unplanned hospitalization deteriorates patients' prognosis. In addition, risk factors were identified. METHODS: A retrospective review of 136 patients treated with palliative radiotherapy or CRT was performed. Inclusion criteria were prescribed total dose at least 30 Gy and outpatient at the start of treatment. Uni- and multivariate analyses were employed. RESULTS: Fifty-eight patients (43%) were hospitalized within 3 months from start of radiotherapy or CRT. Their median overall survival was 6.7 months as compared to 11.1 months in non-hospitalized patients (P = 0.09). The median length of hospitalization was 8 days (range 1 - 61). In patients with possibly treatment-related hospitalization (n = 32), median survival was 5.0 months, significantly shorter than the 11.1 months observed in the remaining patients (P = 0.006). In multivariate analysis, only one variable was significantly associated with higher risk of unplanned hospitalization: previous hospitalization in the last 4 weeks before commencing radiotherapy or CRT. CONCLUSIONS: Unplanned hospitalization occurred frequently in a standard care setting without early involvement of a dedicated palliative team. Patients with preceding hospitalization might represent a group that is particularly vulnerable, thus qualifying for a targeted intervention aiming at continued outpatient care.
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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 | 0.005 |
| 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".