Assessment of Palliative Care in Lung Cancer in Turkey
Bibliographic record
Abstract
OBJECTIVE: To investigate the symptoms of lung cancer in Turkey and to evaluate approaches to alleviate these symptoms. SUBJECTS AND METHODS: This study included 1,245 lung cancer patients from 26 centers in Turkey. Demographic characteristics as well as information regarding the disease and treatments were obtained from medical records and patient interviews. Symptoms were evaluated using the Edmonton Symptom Assessment Scale (ESAS) and were graded on a scale between 0 and 10 points. Data were compared using the χ2, Student t, and Mann-Whitney U tests. Potential predictors of symptoms were analyzed using logistic regression analysis. RESULTS: The most common symptom was tiredness (n = 1,002; 82.1%), followed by dyspnea (n = 845; 69.3%), appetite loss (n = 801; 65.7%), pain (n = 798; 65.4%), drowsiness (n = 742; 60.8%), anxiety (n = 704; 57.7%), depression (n = 623; 51.1%), and nausea (n = 557; 45.5%). Of the 1,245 patients, 590 (48.4%) had difficulty in initiating or maintaining sleep. The symptoms were more severe in stages III and IV. Logistic regression analysis indicated a clear association between demographic characteristics and symptom distress, as well as between symptom distress (except nausea) and well-being. Overall, 804 (65.4%) patients used analgesics, 630 (51.5%) received treatment for dyspnea, 242 (19.8%) used enteral/parenteral nutrition, 132 (10.8%) used appetite stimulants, and 129 (10.6%) used anxiolytics/antidepressants. Of the 799 patients who received analgesics, 173 (21.7%) reported that their symptoms were under control, and also those on other various treatment modalities (dyspnea: 78/627 [12.4%], appetite stimulant: 25/132 [18.9%], and anxiolytics/antidepressants: 25/129 [19.4%]) reported that their symptoms were controlled. CONCLUSION: In this study, the symptoms progressed and became more severe in the advanced stages of lung cancer, and palliative treatment was insufficient in most of the patients in Turkey.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".