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Record W4388627828 · doi:10.33678/cor.2023.026

(Frailty syndrome, what should we know before cardiac surgery)

2023· article· en· W4388627828 on OpenAlexaboutno aff
Petr Smolák, Ján Gofus, Martin Voborník, Martin Děrgel, Salifu Timbilla, Martin Maťátko, Samuel Marcinov, Jiří Manďák, Jan Vojáček

Bibliographic record

VenueCor et Vasa · 2023
Typearticle
Languageen
FieldMedicine
TopicFrailty in Older Adults
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineGynecologyTheologyInternal medicinePhilosophy

Abstract

fetched live from OpenAlex

Syndrom křehkosti je klinickĂ˝ syndrom, ve kterĂŠm jsou přítomny tři nebo vĂ­ce z nĂĄsledujĂ­cĂ­ch kritĂŠriĂ­: neĂşmyslnĂ˝ Ăşbytek hmotnosti 4,5 kg (10 liber) za poslednĂ­ rok, pacientem udĂĄvanĂĄ vyčerpanost, slabĂĄ svalovĂĄ sĂ­la Ăşchopu, pomalĂĄ rychlost chĹŻze a snĂ­ĹženĂĄ fyzickĂĄ aktivita. Prevalence křehkosti u kardiochirurgickĂ˝ch pacientĹŻ je podle dostupnĂ˝ch studiĂ­ od 4,1 % do 46 %. Je spojenĂ˝ s vĂ˝skytem sarkopenie a osteoporĂłzy. Pro diagnostiku v kardiochirurgii je vhodnĂ˝ nĂĄstroj Edmonton Frail Scale, kterĂ˝ je vyvinut pro negeriatrickĂŠ specialisty a poskytuje informace ohledně zĂĄvislosti pacienta na okolĂ­, znalost zvlĂĄdĂĄnĂ­ běžnĂ˝ch dennĂ­ch aktivit a Ăşrovně fyzickĂŠ zdatnosti. Existuje ĹĄirokĂĄ ĹĄkĂĄla dalĹĄĂ­ch nĂĄstrojĹŻ k hodnocenĂ­. Syndrom křehkosti je nezĂĄvislĂ˝ rizikovĂ˝ faktor zvýťenĂŠ morbidity, mortality a prodlouĹženĂŠ doby hospitalizace po kardiochirurgickĂŠ operaci. Tito pacienti majĂ­ vysokĂŠ riziko neĂşspěchu zvolenĂŠho terapeutickĂŠho postupu. ZĂĄkladem péče o rizikovĂŠ pacienty je screening a prevence vzniku syndromu křehkosti. DĂĄ se mu předejĂ­t dostatečnou fyzickou aktivitou, zdravĂ˝m ĹživotnĂ­m stylem a pravidelnĂ˝m kognitivnĂ­m trĂŠninkem. © 2023, ČKS.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.166
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.003

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.

Opus teacher head0.079
GPT teacher head0.331
Teacher spread0.252 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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