Digital language measures capture episodic memory disruptions in people with human immunodeficiency virus: A machine learning study
Bibliographic record
Abstract
Objective: Human immunodeficiency virus (HIV) often affects episodic memory. Yet, standard measures of this domain are derived from clinicians’ simple counts of recalled and omitted pieces of information, undermining robustness, informativeness, and scalability. Here, we present an automated natural language processing (NLP) approach that tackles such limitations. Methods: We recruited 92 participants (50 people living with HIV and 42 controls), who performed a story retelling task. Using NLP tools, we compared the retellings and the original story in terms of verbosity, semantic acuity, and organizational structure. Results: We found that people living with HIV produced fewer nouns and had poorer semantic acuity and organizational similarity. Moreover, machine learning classifiers robustly differentiated between the two groups. Conclusion: These results suggest that our digital approach can reveal fine-grained episodic memory alterations in people living with HIV, offering an objective, scalable, and cost-effective complement to standard cognitive testing.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".