Psychosocial sequelae following cardiac arrest
Bibliographic record
Abstract
Ăvod: SrdeÄnĂ zĂĄstava (SZ) s nĂĄslednou cerebrĂĄlnĂ hypoxiĂ vede k multifaktoriĂĄlnĂmu poĹĄkozenĂ mozku. KognitivnĂ deficit a vyĹĄĹĄĂ mĂra depresivnĂch projevĹŻ jsou nejÄastÄji popisovanĂ˝mi psychickĂ˝mi dĹŻsledky SZ. CĂlem pĹedklĂĄdanĂŠ studie je charakterizovat psychosociĂĄlnĂ dĹŻsledky SZ.Metody: PrĹŻzkumnĂŠho ĹĄetĹenĂ se ĂşÄastnilo 113 osob. K 62 pacientĹŻm po SZ bylo dle demografickĂ˝ch charakteristik a premorbidnĂ inteligenÄnĂ ĂşrovnÄ pĹiĹazeno 51 zdravĂ˝ch jedincĹŻ. ĂÄastnĂkĹŻm byly pĹedloĹženy testy kognitivnĂ vĂ˝konnosti (Montreal Cognitive Assessment, MoCA), depresivnĂch (Beck Depression Inventory-II, BDI-II) a ĂşzkostnĂ˝ch projevĹŻ (State-Trait Anxiety Inventory, STAI) a sociĂĄlnĂch faktorĹŻ krize stĹednĂho vÄku (Ĺ kĂĄla faktorĹŻ krize stĹednĂho vÄku, Ĺ KSV).VĂ˝sledky: AnalĂ˝za ukĂĄzala, Ĺže pacienti po SZ vykazujĂ niŞťà kognitivnĂ vĂ˝konnost (p = 0,016) a vyĹĄĹĄĂ mĂru symptomĹŻ aktuĂĄlnĂ Ăşzkosti (p = 0,023). V Ăşrovni depresivnĂch symptomĹŻ (p = 0,435) a v dlouhodobĂŠ Ăşzkostnosti (p = 0,542) se pacienti po SZ neliĹĄili od kontrolnĂho souboru. Ex-post facto analĂ˝za zaloĹženĂĄ na logistickĂŠ regresi poukazuje na to, Ĺže nejsilnÄjĹĄĂm psychologickĂ˝m prediktorem SZ je pohlavĂ (OR = 4,45) a mĂra aktuĂĄlnÄ proĹžĂvanĂŠ Ăşzkosti (OR = 0,50). AnalĂ˝za diskriminaÄnĂ funkce ukazuje, Ĺže predikce vzniku SZ je zĂĄvislĂĄ na vÄku, kognitivnĂ vĂ˝konnosti a na mĂĹe aktuĂĄlnĂ Ăşzkosti (λ = 0,81, p = 0,028).ZĂĄvÄr: VĂ˝sledky studie ukazujĂ, Ĺže SZ mĂĄ vĂ˝znamnĂ˝ vliv na rozvoj neŞådoucĂch kognitivnĂch a neuropsychiatrickĂ˝ch projevĹŻ. ZaĹazenĂ psychosociĂĄlnĂ pĂŠÄe a neuropsychiatrickĂŠ lĂŠÄby do komplexnĂ pĂŠÄe o pacienty po SZ je ŞådoucĂ.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".