Da Covid-19 á condición post-Covid-19: disfunción cognitiva e impacto emocional tras hospitalización
Bibliographic record
Abstract
Obxectivos: (1) determinar a frecuencia e perfil de disfunción cognitiva das persoas que requiriron hospitalización, aos 6-9 meses da alta, atendendo á sintomatoloxía neurolóxica durante a fase aguda; (2) determinar a prevalencia de síntomas de estrés postraumático; e (3) investigar a relación entre as queixas cognitivas subxectivas, a gravidade da infección, o impacto emocional e o rendemento cognitivo obxectivo. Método: Participaron 44 pacientes (50% mulleres; 50,9 ± 6,14 anos). Cumprimentaron a avaliación cognitiva de Montreal (MoCA), a escala de impacto de eventos revisada (IES-R) e o cuestionario de queixas subxectivas (MFE-30). Resultados: O 65,9% evidenciou deterioración cognitiva, sen diferenzas por sintomatoloxía neurolóxica. O perfil de disfunción cognitiva amosou alteración da capacidade de aprendizaxe verbal, déficits executivos (fluidez), visoespaciais, e de memoria de traballo. O 48,8% presentou sintomatoloxía postraumática. As variables predictoras das queixas subxectivas (MFE-30) foron a sintomatoloxía postraumática (IES-R) e o rendemento cognitivo (MoCA). Conclusións: Unha porcentaxe importante dos participantes amosan disfunción cognitiva leve-moderada, non asociada ao cadro neurolóxico na fase aguda. A metade amosa sintomatoloxía postraumática. O rendemento cognitivo observado e o impacto emocional, pero non o cadro clínico na fase aguda, explican as queixas cognitivas aos 6-9 meses. Constátase o carácter multifactorial da post-Covid-19
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".