A Cross-sectional Study to Assess the Association between the Cognitive Function with Severity of Dependence and Motivation to Quit in Patients with Substance Use Disorder
Bibliographic record
Abstract
Introduction: Long-term substance abuse can impact an individual’s cognition and higher mental abilities, which can, in turn, affect their judgment-making capacity and lead to a higher chance of relapse. This is because the individual’s motivation to quit is linked to their cognitive functioning. Aim: To assess the association between cognitive function, the severity of dependence, and the motivation to quit in patients with Substance Use Disorder (SUD). Materials and Methods: This was a cross-sectional observational study conducted in a tertiary care hospital in Eastern India between October 2018 and September 2020. The study includes total 83 patients diagnosed with substance dependence according to the International Classification of Diseases-10 guidelines by the treating psychiatrist, and admitted to the inpatient unit of the Department of Psychiatry Socio-demographic details such as age, gender, background, marital status, primary substance, and duration of use were obtained using a semi-structured questionnaire. Cognitive functions were assessed using the Montreal Cognitive Assessment Scale (MoCA) and the Frontal Assessment Battery (FAB). The severity of dependence was assessed using the Leeds Dependence Questionnaire. Motivation to quit substances was assessed using the Stages of Change Readiness and Treatment Eagerness Scale. Statistical Package for the Social Sciences(SPSS) v23 was used for data analysis. Results: All subjects included in the study were male. Approximately 56 (67.5%) were admitted for alcohol dependence, followed by opioid dependence. The majority of the study sample had moderate severity of dependence, with 52 (62.7%) participants showing good motivation. A total of 53 (63.9%) participants had global cognitive deficits, and frontal lobe dysfunction was seen in 41 (49.4%) of the study sample. Both cognitive functioning and frontal lobe functioning were significantly associated with motivation to quit. The severity of dependence on the substance was not significantly associated with either cognitive function or frontal lobe functioning. Conclusion: Out of the total sample, almost two-thirds had cognitive deficits, and half had frontal lobe dysfunction. The correlation between poor motivation and cognitive impairment, as well as frontal lobe dysfunction, was found to be statistically significant, indicating that poor motivation to quit substances in patients may be affected by cognitive process dysfunction or frontal lobe damage.
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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 | 0.010 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".