Use of the scored Patient-Generated Subjective Global Assessment (PG-SGA) to characterize cachexia in newly diagnosed advanced cancer patients
Bibliographic record
Abstract
9574 Background: Our objective was to evaluate whether the scored PGSGA questionnaire in advanced cancer patients (ACP) might relate better then weight loss (WL) alone to the nutritional, functional, biological and quality of life features of cachexia (C) and to some complications related to this syndrome. Methods: 214 newly diagnosed ACP with non-small cell lung and gastrointestinal primaries were categorized according to PG-SGA triage intervals of 0–1, 2–8 and ≥9 and also according to WL ≤5% or >5%. Baseline assessments included: hand-grip strenght, body composition by DXA, selective measures of symptom and quality of life (QoL), CBC and differential counts, albumin and CRP. Survival hospitalization rates and data on chemotherapy tolerability were recorded during patient follow-up. Beta coefficients (β), odds ratios (OR), and hazard ratios (HR) were estimated to compare patients with >5% WL to those with ≤5% WL and to compare patients with PGSGA of 0–1 to those with 2–8 and ≥9 scores. All analyses were controlled for gender, age, diagnosis (lung/GI), treatment (radio/chemo), survival (at 8 weeks), and medications. Results: PGSGA was better than the simple recording of WL in defining a population of patients that differed for WBC 109/L(>5% WL β: 0.25 vs. 2–8 PGSGA β: 0.57 and ≥9 PGSGA β: 1.72), CRP mg/L (4.12 vs. 2.16 and 17.49), albumin g/L(-0.63 vs -2.60 and -4.45); weakness 0–10 (1.57 vs.1.56 and 3.32), anorexia 0–10 (2.36 vs. 2.36 and 5.17); Brief Fatigue Inventory 0–90 (17.75 vs. 9.89 and 25.15); McGill QoL 10–0 (-0.95 vs. -0.64 and -2.29); grip strength lbs.(-4.04 vs. -8.82 and -8.06); body fat kg. ( -8.74 vs. -5.94 and -11.72). PGSGA was able to better identify patients with higher rates of both hospitalization (2.6 vs. 1.62 and 9.46) and dose reduction of chemotherapy (1.2 vs. 0.58 and 1.74). Finally, PGSGA was able to better characterize patient survival as compared to WL alone (>5% WL HR: 1.85; 2–8 PGSGA HR: 1.6 and ≥9 PGSGA HR 3.35). Conversely, WL alone was associated with higher probability of a sarcopenia diagnosis by DXA (>5% WL OR: 1.56)Conclusions: Our data support the use of the PGSGA versus the simple recording of WL for identifying C, monitoring its clinical course and predicting possible complications of this syndrome in ACP. 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 | 0.001 | 0.003 |
| 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 | 0.000 | 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".