Domain‐specific self‐ and informant‐reported cognitive complaints across the Alzheimer’s disease spectrum
Bibliographic record
Abstract
Abstract Background A growing number of studies are currently investigating whether Subjective Cognitive Decline (SCD) may represent an early indicator of Alzheimer’s disease (AD). Due to the high frequency of amnestic AD dementia, SCD research is highly focused on episodic memory. Non‐amnestic complaints are less studied, although patients or their families often report difficulties in retrieving words, planning or spatial orientation, to name a few (e.g., Valech et al., 2018, J Alzheimers Dis). Furthermore, few studies have investigated which source of complaint is the most accurate. In this study, we aimed to measure how accurately self‐ and informant‐reported complaints in different cognitive domains can discriminate individuals along the AD continuum and controls. Method We selected four groups of participants from the ADNI cohort: amyloid‐positive (Aβ+) individuals with AD, mild cognitive impairment (MCI) or cognitively normal (CN), and amyloid‐negative (Aβ‐) CN controls. Self‐ and informant‐reported complaints were measured with the Everyday Cognition questionnaire in four domains, namely, memory, language, visuospatial abilities, and executive functions. Receiver operating characteristic curves analysis was performed to evaluate the accuracy of the ECog scores in discriminating the groups. Result Self‐ and informant‐reported complaints were distributed as follows: memory, language, executive, and visuospatial (from the most severe to the least severe). ECog‐Subject scores globally distinguished groups little better than chance. The Memory score performed best among the ECog‐Subject scores, but the accuracy was low to moderate. In the other three domains, most AUCs were below 0.70 and specificities always below 70%. All ECog‐StudyPartner scores (i.e., in all four cognitive domains) could discriminate MCI and AD from CN, as well as AD from MCI, with good to excellent accuracy (all AUCs between 0.72 and 0.98). However, no ECog‐StudyPartner score seemed useful in distinguishing Aβ+/CN and Aβ‐/CN subjects (all AUC<0.55, all specificity indices <0.22). Conclusion Self‐reported SCD may have limited utility for both early diagnosis and the inclusion of subjects in research protocols. Individuals with early‐stage AD may already present reduced awareness of their cognitive decline (e.g., Cacciamani et al., 2021, Alzheimer’s Res. Ther). For this reason, it seems crucial to consider informant‐reported complaints, both amnestic and non‐amnestic.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".