QUEIXAS SUBJETIVAS DE MEMÓRIA: SINTOMAS DEPRESSIVOS, ANSIÓGENOS OU DÉFICES MNÉSICOS OBJETIVOS?
Bibliographic record
Abstract
As queixas subjetivas de memória (QSM) na população idosa despertam, na comunidade científica, vários desafios. Em particular a diferenciação entre as QSM com a sintomatologia depressiva e ansiógena e o prejuízo mnésico objetivo. Este estudo transversal foi realizado com 620 participantes com mais de 54 anos (74.04 ± 10.41 anos; 72.4% do sexo feminino). Os instrumentos utilizados foram o Mini Mental State Examination (MMSE), o Montreal Cognitive Assessment (MoCA), a Escala de Queixas de Memória (EQM), a Escala de Depressão Geriátrica (EDG) e o Inventário de Ansiedade Geriátrica (IAG). As QSM estavam presentes em 78.9% (n = 489), os sintomas depressivos em 46.3% (n = 287) e os ansiógenos em 51.1% (n = 317). Os participantes com QSM obtiveram pontuações inferiores no MMSE (24,57 ± 5,65 vs 25,88 ± 5,36, p < 0,01), bem como, no MoCA (17,63 ± 7,86 vs 20,34 ± 7,84, p< 0,01). A escolaridade [β = 0,14, 95% intervalos de confiança (IC) = -0,823-0,475], o MMSE (β = -0,11, 95% IC = 0,034-0,241) e a EDG (β = 0,40, 95% IC = -0,112-0,59) revelaram-se preditores significativos das QSM. A depressão e a ansiedade parecem ter um efeito potenciador das QSM e associam-se a um pior desempenho cognitivo, sugerindo que as intervenções direcionadas para esses fatores se assumam como uma questão estratégica na promoção do envelhecimento saudável.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".