Sarcoma Does Not Predict Malnutrition in Cancer Patients: A Retrospective Cohort Study
Bibliographic record
Abstract
The association between inflammation, malnutrition, and cancer is not well understood. The aim of this study was to examine the association between inflammatory-type cancer and diagnosed malnutrition, albumin level, and age in patients with cancer. Malnutrition and cancer diagnoses were obtained using data from hospital medical records in patients admitted for cancer between Oct. 2017 and Dec. 2018. Demographics, as well as the first and lowest albumin levels were also obtained. A simple t-test is processed between age and malnutrition status. Also a chi-square test of independence was performed to examine the relation between malnutrition and hypoalbuminemia status. Logistic regression was conducted between malnutrition status, sarcoma cancer, age, and hypoalbuminemia. The study included 4034 patients (2084 males, 1949 females). Approximately 4% of the patients were diagnosed with malnutrition. Logistic regression on malnutrition status, sarcoma, age, and hypoalbuminemia showed a significant association on global test (3, 2433, P-value = 0.013). Hypoalbuminemia (< 3.9 g/dL lower-limit) was significantly associated with malnutrition (X2 1, 2433 P-value = 0.0156). Sarcoma diagnosis was not significantly associated with malnutrition (X2 1, 2433 P-value = 0.267). Age is not significantly related to malnutrition status (X2 1, 2433 p-value = 0.449). A t-test was also performed malnutrition vs no malnutrition on age, resulting in a marginally significant association for malnutrition group (M = 65.33, SD = 15.50) vs no malnutrition group (M = 67.32, SD = 17.55) (t(1) = 3.7212, P = 0.0537). Sarcoma is not significantly associated with an increased risk of malnutrition. Cancer patients with hypoalbuminemia have a higher risk for malnutrition compared to the patients with normal albumin level. Additionally, age may be a predictor for cancer patients’ risk of in-hospital malnutrition. None.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".