Associations between pretreatment nutritional assessments and treatment complications in patients with stage I-III non-small cell lung cancer: A systematic review
Bibliographic record
Abstract
BACKGROUND: Patients with stage I-III non-small cell lung cancer (NSCLC) are often nutritionally depleted and therefore at high-risk for treatment complications. Identifying these patients before the start of treatment is important to initiate preventive interventions for better treatment outcomes. This study aimed to evaluate which outcome variables of pretreatment nutritional assessments are associated with posttreatment complications in patients with stage I-III NSCLC, as well as to identify cut-off values for clinical risk stratification. METHODS: In this systematic review, PubMed, Embase, and Cinahl databases were searched for eligible studies published up to March 2021. Studies describing the association between pretreatment nutritional assessment and treatment complications in patients with NSCLC were included. Methodological quality of the included studies was assessed using the Newcastle-Ottawa Scale for cohort studies. RESULTS: A total of 23 studies were included, which merely focused on surgical treatment for NSCLC. Methodological quality was poor in thirteen studies (57%). Poor outcomes of body mass index, sarcopenia, serum albumin, controlling nutritional status, prognostic nutrition index, nutrition risk score, and (geriatric) nutrition risk index were associated with a higher risk for treatment complications. Cut-off values for pretreatment nutritional assessment were reported in a limited number of studies and were inconsistent. CONCLUSION: Poor outcomes of pretreatment nutritional assessments are associated with a higher risk for posttreatment complications. Further research is needed on the ability of easy-to-use pretreatment nutritional assessments to accurately identify patients who are at high risk for treatment complications, as high-risk patients may benefit from pretreatment interventions to improve their nutritional status.
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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 | 0.007 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.009 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".