Do most newly diagnosed advanced NSCLC patients need nutritional intervention?
Bibliographic record
Abstract
9108 Background: Depletion of nutritional reserves and significant weight loss are commonly noted in patients (pts) with non-small cell lung cancer (NSCLC). The Patient Generated Subjective Global Assessment (PG-SGA) is a nutritional screening tool for cancer pts, recommended by the Oncology Network of Dietitians of Canada and the American Dietetic Association. The PG-SGA categorizes total scores into 4 ranges for nutritional triage: 0–1 requires no intervention, 2–3 requires education, 4 - 8 requires intervention by a dietitian, and =9 requires urgent symptom control and nutrition intervention. (Ottery, 2000). Methods: We determined the prevalence of malnutrition in 92 newly diagnosed advanced NSCLC pts (stages 3 / 4) in an outpatient clinic who had completed a baseline PG-SGA. We also investigated the association between CRP (an inflammatory marker which correlates with poor prognosis) and the PG-SGA. PG-SGA score was based on the sum of 4 subscales: symptoms, weight history, food intake and functional status. Results: 92 pts (M 48, F 44) aged 65 ±11 years were studied. 21 (23%) pts had a PG-SGA score of 0–3, 23 (25%) 4–8, and 48 (52%) of 9 or greater. The most common symptoms accounting for a high PG-SGA score were: no appetite 37 (40%), pain 27 (29%), constipation 26 (28%), feeling full 24 (26%), dry mouth 22 (24%) and taste changes 19 (21%). 51 (55%) pts lost 0–4.9% of their body weight in the past month, 17 (19%) had a weight loss of 5–9.9% and 24 (26%) had a weight loss =10 %. In pts with a PG-SGA score of 0–3 the median CRP was 7.0 mg/L (range: 0.7–66.0), in those with a score of 4–8 the median CRP was 41.8 mg/L (0.8–266.1) and in those with a score of =9 the median CRP was 18.5 mg/L (0.3–219.0) (p=0.02). Conclusion: At time of diagnosis, 77% of advanced NSCLC pts were in need of nutritional intervention; 52% required urgent intervention. The PG-SGA is a simple screening tool which should be incorporated into patient care in outpatient oncology clinics. No significant financial relationships to disclose.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".