The impact of neurocognitive function on health utility scores (HUS) in Stage 4 (S4) lung cancer patients (pts) with and without brain metastases (BrM).
Bibliographic record
Abstract
225 Background: As targeted therapies for S4LC pts have improved survival, the impact of health-related quality of life (HRQoL) in these pts grows in importance. In pts with BrM, loss of NCF may affect HRQoL significantly. We evaluated the relationship between NCF and HUS as a measure of HRQoL in S4LC pts. Methods: Self-reported HUS data from EQ5D-3L were obtained cross-sectionally from S4LC pts with (BrM) or without BrM (non-BrM). NCF was measured using the Hopkins Verbal Learning Test – Revised (HVLT-R), the Controlled Oral Word Association Test (COWAT) and Trail Making Tests (TMT-A/B). NCF scores were correlated with HUS (Pearson Coefficient, R). Results: BrM (n = 54) and non-BrM (n = 40) patients had similar demographics- overall median age was 61 (range 33-89) years; 59% were female; 45% had EGFR/ALK alterations; BrM pts were treated with whole brain (n = 20), stereotactic radiation (n = 19), both (n = 6), or sole use of systemic agents (n = 6); 3 were observed; 7 had BrM resection. Overall HUS were similar between BrM and non-BrM groups (mean HUS (mHUS): 0.77 vs. 0.78; p = 0.86). However, pts with stable brain disease had higher HUS than those with progressive brain disease (mHUS: 0.80 (n = 36) vs 0.69 (n = 17); p = 0.045). There was a trend towards lower HUS in symptomatic vs. asymptomatic BrM (mHUS: 0.70 (n = 10) vs. 0.78 (n = 43); p = 0.07). Multiple correlations between NCF scores and HUS were found. HLVT-Total Recall correlated with HUS in BrM but not non-BrM (R=0.35, p = 0.01; vs. non- R=0.04, p = 0.84 respectively) as did the HLVT-Recognition (BrM: R=0.32, p = 0.03 vs. non-BrM: R=0.13, p = 0.51). In contrast, TMT-A/B were associated with HUS in both BrM (p = 0.03, 0.06) and non-BrM pts (p = 0.001, 0.03). COWAT was associated with HUS only when all pts were analyzed together (p = 0.04). Conclusions: In S4LC pts, HUS were correlated with multiple measures of NCF. The impact of uncontrolled brain disease and poor NCF on HUS demonstrates that this measure has clinical utility, with important HRQoL implications as metastatic lung cancer pts live longer.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".