Determine the Symptom Intensities, Performance and Hopelessness Levels of Advanced Lung Cancer Patients for the Palliative Care Approach
Bibliographic record
Abstract
This research was conducted descriptively to determine the symptom intensities, performance and hopelessness levels of advanced lung cancer patients for the palliative care approach. The research sample consisted of 130 patients with advanced lung cancer, who were selected from 600 lung cancer populations in thoracic surgery and intensive care, outpatient chemotherapy, oncology in a university hospital in Turkey. Ethics Committee permission and the patients’ written consent was obtained. Study data were collected face to face between January 2020 and July 2020 using the Edmonton Symptom Assessment System, Karnofsky Performance and Beck Hopelessness Scale. The mean age of the patients was 62.68 ± 8.867, 72.3% were males, and 89.2% were not currently working. The most common symptom in the patients was found to be fatigue 5.46 ± 2.12, worsening in general health and well-being 5.69 ± 1.87, loss of appetite 5.40 ± 2.59, and total symptom score 47.17 ± 19.03. Feelings and expectations about the future 1.40 ± 1.66, loss of motivation 3.43 ± 2.41, hope 2.05 ± 1.75, and total score of hopelessness 7.41 ± 6.01. There was a positive correlation between the patients’ hopelessness level and their symptom burden, and a negative correlation was found with Karnofsky performance ( P < .05). A significant difference was found between the patients’ age, months since diagnosis, gender, education and employment status, stage of the disease, presence of metastases and analgesic use, and hopelessness scores ( P < .05). It was determined that the symptom burden of patients with advanced lung cancer increased and as their Karnofsky performance decreased, their hopelessness level further increased. Hopelessness scores are affected by the socio-demographic and disease variables of the patients.
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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 | 0.000 | 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".