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The impact of neurocognitive function on health utility scores (HUS) in Stage 4 (S4) lung cancer patients (pts) with and without brain metastases (BrM).

2017· article· en· W2598968435 on OpenAlexaff
Brandon Tse, Vivian Tam, Tiffany Tse, Lin Lü, Michael Borean, Emily Tam, Catherine Labbé, Mark Doherty, Penelope Ann Bradbury, Natasha B. Leighl, M. Catherine Brown, Wei Xu, Doris Howell, Geoffrey Liu, Grainne M. O’Kane

Bibliographic record

VenueJournal of Clinical Oncology · 2017
Typearticle
Languageen
FieldMedicine
TopicLung Cancer Research Studies
Canadian institutionsUniversity Health NetworkPrincess Margaret Cancer Centre
Fundersnot available
KeywordsMedicineNeurocognitiveInternal medicineQuality of life (healthcare)Lung cancerAsymptomaticCancerOncologyStage (stratigraphy)DiseaseGastroenterologyCognition

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.011
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.123
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.164
GPT teacher head0.583
Teacher spread0.419 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2017
Admission routes1
Has abstractyes

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