Chemotherapy Regimen and Incidence of Malnutrition after Chemotherapy in Non-small Cell Lung Cancer
Bibliographic record
Abstract
Lung cancer is the leading cause of death worldwide and in Thailand. The treatment of non-small cell lung (NSCLC) with chemotherapy might affected on nutritional status which could correlate with the treatment response and quality of life. Thus, the objectives of this research were to study the nutritional status of patients after chemotherapy and the incidence of malnutrition. This retrospective longitudinal descriptive study was gathering the information from medical records January, 2013 to December, 2014. A 114 patients were met the inclusion criteria which were completed 4 or 6 cycles of treatment. Body mass index (BMI) and percentage of weight loss were used to assess the nutritional status. Malnutrition was classified when BMI under 18.5 kg/m2 and weight change more than 5%. The average age, baseline weight and BMI were 60.4±10.1 year, 55.0±9.0 kg and 22.0±2.5 kg/m2, respectively. The incidence of malnutrition was 6.1%. Mean BMI decreased from 22.0 kg/m2 to 21.5 kg/m2 after chemotherapy. Patients treated with carboplatin plus paclitaxel showed the highest change of BMI (-0.6 kg/m2) and docetaxel regimen showed the highest incidence of malnutrition (18.8%). According to the base agents; taxane-based regimen showed the most effect on nutritional status and 85% of those patients were malnutrition. In conclusion, docetaxel and carboplatin plus paclitaxel highly affected on nutritional status. In patients treated with those regimens, they should be closely monitored and gave an adequate nutritional advice for the better treatment response, decrease the side effect from chemotherapy and improve patients’ quality of life.
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 | 0.000 | 0.001 |
| 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 | 0.000 |
| 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".