Development of a rapid screener through network analysis to identify central cognitive complaints in haemodialysis patients: a cross-sectional study
Bibliographic record
Abstract
OBJECTIVES: Cognitive impairments and cognitive complaints are commonly present in dialysis patients and can affect clinical, functional, occupational, and psychosocial well-being. It is important to screen for patients' cognitive status as it offers a gateway to specialty referral, prevention or rehabilitation programmes, and personalisation of clinical care. The Patient's Assessment of Own Functioning Inventory (PAOFI) is a comprehensive questionnaire that assesses patient-reported difficulties in memory, language, motor/sensory-perceptual skills and higher-level cognitive function. In the current study, we adopted network analysis to identify central cognitive complaints in dialysis patients and derived a PAOFI short form (PAOFI-SF) based on these core symptoms to improve screening efficiency in real-world renal settings. DESIGN: Multicentre, cross-sectional study. SETTING: Participants were recruited from 10 community-based dialysis centres in Singapore, from May to November 2022. PARTICIPANTS: A total of 369 eligible haemodialysis patients were invited to join the study, and 268 completed the measures (response rate 72.6%). OUTCOME MEASURES: Cognitive assessment tools including the PAOFI and the Montreal Cognitive Assessment were administered. RESULTS: Based on the PAOFI measure, 98 participants (36.6%) endorsed the presence of three or more complaints, indicating clinically significant cognitive complaints. Network analysis identified five central cognitive complaints among dialysis patients: problem-solving difficulty, difficulty following instructions, forgetting how to do tasks, difficulty being understood, and forgetting people known years ago. These core items were combined into a five-item short form of PAOFI, which showed good reliability and validity, and an area under the curve of 83.4% in identifying clinically significant cognitive complaints. The optimal cut-off point of the short form was 11.5 (out of 30), with a specificity of 89.5%, sensitivity of 63.9%, positive predictive value of 77.5% and negative predictive value of 81.4%. This cut-off point also predicted objective cognitive performance even after controlling for sociodemographic and clinical confounders. CONCLUSIONS: Pending future replication and external validation, the PAOFI-SF may be suitable for use in renal care settings as an initial screening tool to identify patients with cognitive complaints and increased risk of objective cognitive impairments.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".