Évaluer un arbre diagnostique permettant le repérage de troubles neurocognitifs majeurs en soins primaires et permettant un lien ville-hôpital
Bibliographic record
Abstract
Introduction: to improve the identification of Major Neurocognitive Disorders (MND), simplified screening tools are needed. Shorter tests with high diagnostic performance should be implemented in Primary Health Care, where general practitioners serve as the main providers. However, Delirium is not included in the commonly used screening tests. It differs from MND by its sudden onset and fluctuating course and constitutes a differential diagnosis of MND. We propose a diagnostic algorithm to enhance the identification of MND in primary care, incorporating systematic screening for Delirium. The objective of our study was to evaluate the effectiveness and feasibility of this diagnostic algorithm in diagnosing MND or its association with Delirium. Methods: a diagnostic validation study was conducted. The diagnostic algorithm was developed prior to participant recruitment. It consisted of the use of four tests routinely employed in clinical practice: the Confusion Assessment Method (CAM), the Cognitive Disorders Examination (CODEX), the Montreal Cognitive Assessment (MoCA), and the Informant Questionnaire on Cognitive Decline in the Elderly – Revised (IQCODE-R). These tests were administered to individuals aged 70 years and older in general practice offices and in acute geriatric hospital units. Oral and written informed consent were obtained from all participants. We assessed the agreement between the results obtained from the diagnostic algorithm and those derived from the reference standard diagnosis, namely the fifth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5). Agreement was evaluated using Cohen’s kappa coefficient. Results: one hundred and thirty participants were included in the study. The mean age at inclusion was 82.7 years; 55 were men and 75 were women. Sixty-eight participants were hospitalized, and 62 were recruited in general practice settings. Comparison of the results with the reference standard yielded a Cohen’s kappa coefficient of 0.89, indicating excellent diagnostic agreement. The diagnostic algorithm enabled identification of MND or Delirium within a mean time of 12.6 minutes. Discussion and Conclusion: the diagnostic algorithm provides physicians with a simple and effective tool for identifying MND and Delirium using tests previously validated in France. To our knowledge, this study is the first to combine these screening approaches. Future research is needed to assess the implementation of this combined screening strategy in routine medical practice, particularly in general practice settings, in order to determine whether its use is suitable and acceptable for practitioners.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".