Health state utility assessment for low-grade glioma.
Bibliographic record
Abstract
e14039 Background: Low-grade gliomas (LGGs, WHO Grade II gliomas) undergo malignant transformation, portending a median survival of 5-15 years. Though resection and adjuvant therapies have significantly improved survival, there are increased risks of long-term neurological decline and treatment-related side effects. As a result, not only quantity but also quality of life (QoL) must be considered when appraising therapeutic paradigms. Health state utility assessments can quantify society’s perceptions of given health states, supporting quality-adjusted life years (QALYs) and cost-effectiveness analyses. The primary and secondary objectives were to calculate LGG health states utility values (UVs) and interpret differences between health states, respectively. Methods: Health state descriptions were developed from a comprehensive literature review, expert input, and focus groups involving neuro-oncology providers and patient advocates. Healthy, adult participants were surveyed through an online REDCap form. Two preference elicitation techniques, visual analog scale (VAS) and time trade-off (TTO), were used to derive UVs of ten LGG health states. UVs are anchored at 0 representing death and 1 for perfect health. Mann-Whitney U tests compared UVs across health states. Results: Ninety-eight participants were included. Most participants were male (52%), aged 18-29 (81.6%), and White or Asian (78%). Stable LGG had the highest UVs (Mean [SD]: VAS- 0.64 [0.21]; TTO- 0.54 [0.42]) compared to Progressive LGG (VAS- 0.44 [0.26], p<0.0001; TTO- 0.36 [0.37], p<0.0001), Stable LGG with Chemoradiation (VAS- 0.43 [0.23], p<0.0001; TTO- 0.32 [0.34], p<0.0001), and Stable LGG with Motor (VAS- 0.45 [0.23], p<0.0001; TTO- 0.23 [0.32], p<0.0001) and Language (VAS- 0.52 [0.22], p<0.0001; TTO- 0.43 [0.40], p=0.04) Deficits. Progressive LGG UVs were not statistically different from those in Progressive LGG with Motor (VAS- 0.43 [0.23], p=0.69; TTO- 0.32 [0.34], p=0.73), Language (VAS- 0.43 [0.23], p=0.62; TTO- 0.32 [0.34], p=0.60), or Visual (VAS- 0.43 [0.23], p=0.78; TTO- 0.32 [0.34], p=0.60) Deficits. Radiation negatively impacted UVs, as UVs of Stable LGG with Chemoradiation (VAS- 0.43 [0.23]; TTO- 0.32 [0.34]) were significantly lower than UVs of Stable LGG with Chemotherapy alone (VAS- 0.43 [0.23], p=0.018; TTO- 0.32 [0.34], p=0.28) on VAS. Conclusions: These are the first-ever reported LGG health state UVs. These data will enable future QALY and cost-effective analyses, which have increasing importance as new investigational therapies become available for LGG.
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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 | 0.011 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 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".