Association between diffusion tensor imaging analysis along the perivascular space (DTI-ALPS)-based glial-lymphatic dysfunction and cognitive impairment in non-small cell lung cancer
Bibliographic record
Abstract
OBJECTIVE: To investigate the correlation of glial-lymphatic (glymphatic) system function with cognitive deficits in non-small cell lung cancer (NSCLC). METHODS: Data (demographic, clinical, and magnetic resonance imaging [MRI] information) from 83 NSCLC cases and 96 healthy controls were retrospectively analyzed. We evaluated glymphatic activity by using the diffusion tensor imaging analysis along the perivascular space (DTI-ALPS) index and cognitive function with the Montreal Cognitive Assessment (MoCA). Medial Temporal Atrophy (MTA) and Fazekas scores were also rated. Statistical analyses included inter-group comparisons, partial correlation assessments, mediation modeling, and regression to identify predictors of cognitive impairment. RESULTS: NSCLC patients had higher MTA and Fazekas scores but lower MoCA and ALPS index scores than controls (all P < 0.05). The ALPS index was symmetrically reduced in both hemispheres, correlating positively with MoCA (r = 0.276, P = 0.012). In the mediation model, the ALPS index exhibited a partial mediating role (4.6%) in the NSCLC-MoCA association. Older age was an independent predictor of cognitive impairment (odds ratio [OR]: 1.229; 95% confidence interval [CI]: 1.111-1.360). CONCLUSION: In NSCLC patients, glymphatic dysfunction was associated with cognitive impairment, and the DTI-ALPS index may facilitate early detection of these deficits. Advanced age remains a major contributing risk factor.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".