Characterization of cognitive profile in young patients in Buenos Aires, Argentina: preliminary analysis of 5 years and comparing pre‐pandemic and in‐pandemic period
Bibliographic record
Abstract
Abstract Background Cognitive manifestations are an increasingly frequent reason for consultation among young adults ranging from a subjective cognitive complaint (CC), mood or behavioral disorders to attention disorders or other cognitive functions with an impact on their functionality. The objective of this work is to analyze the cognitive profile of patients evaluated during last 5 years, characterize them and compare the results before the COVID‐19 pandemic and after. Method 742 cognitive evaluations were analyzed from a database of patients between 40 and 60 years old who consulted for CC and were evaluated with a standard neurocognitive battery (SNB) between July 2017 and July 2022. Demographic data, screening test (MMSE or MoCA), Beck Depression Inventory (BDI‐2) and/or Hospital Depression and Anxiety Scale (HAD), Wender Utah Rating Scale (WURS) and final assessment diagnosis including criteria for cognitive disorders and depression according to DSM V were performed. Period from 2017 to 2019 was considered pre‐pandemic COVID‐19 period and period from 2020 to 2022 as pandemic period. Result 65.6% were female. Pre‐pandemic, 441 patients were evaluated and 296 during pandemic. The average MMSE/MOCA score was: MMSE (328 total): 26.8 and MOCA (402 total): 24.4. Percentage of normal SNB was 17%, of “cognitive deficit”, without conforming deterioration was 36.8%, mild cognitive disorder (mCD) was 43.4%, mayor cognitive disorder (MCD) greater than 2.6%. Of all the patients, depression diagnosis by Beck/ HAD or DSM V was 59.3%. In addition, 25% of the patients had history of TDAH (Attention deficit disorder and hyperactivity) according to WURS. Only 2 patients were evaluated for Intellectual Disability. Conclusion In this preliminary work, we detected that a high percentage of young patients with CC present some alteration in their cognitive function and almost 60% meet criteria for depression that could impact in these functions. In addition, 25% of patients had a history of TDAH. No significant difference was found between pre and pandemic. We are expanding the sample to be able to characterize the alterations with more specificity.
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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 | 0.000 | 0.001 |
| 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) | 0.001 | 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".