Prevalence of neurocognitive impairments in adults with chronic pain: A cross-sectional study
Bibliographic record
Abstract
Background: There is a strong link between chronic pain and neurocognitive impairment. The co-occurrence of the two disorders often leads to a poor quality of life and significant disability. Aim: To determine the prevalence of neurocognitive impairments in adults with chronic pain. Setting: The study was conducted at a tertiary hospital in Pietermaritzburg, KwaZulu-Natal. Methods: This cross-sectional study was conducted at a pain clinic within a tertiary hospital in Pietermaritzburg, KwaZulu-Natal. Participants were required to be clinic attendees, proficient in English, and have a minimum of a Grade 7 education. Exclusion criteria included neurological disorders, significant language barriers, or ineligible age. Recruitment used purposive sampling with informed consent. Data were collected using socio-demographic and clinical questionnaires, namely, the Mini-Mental State Examination (MMSE), the Montreal Cognitive Assessment (MoCA), and the Physical Self-Maintenance Scale (PSMS). The primary outcome was the prevalence of neurocognitive impairment; secondary outcomes examined associations with demographic and clinical factors using both descriptive and inferential statistics. Results: = 77) of participants screened positive for neurocognitive impairment on MoCA and 55.2% on MMSE. Conclusion: Chronic pain is associated with impairments in neurocognitive performance, particularly in short-term memory and executive functioning. Contribution: = 0.04 for MMSE).
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 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".