(Frailty syndrome, what should we know before cardiac surgery)
Bibliographic record
Abstract
Syndrom kĹehkosti je klinickĂ˝ syndrom, ve kterĂŠm jsou pĹĂtomny tĹi nebo vĂce z nĂĄsledujĂcĂch kritĂŠriĂ: neĂşmyslnĂ˝ Ăşbytek hmotnosti 4,5 kg (10 liber) za poslednĂ rok, pacientem udĂĄvanĂĄ vyÄerpanost, slabĂĄ svalovĂĄ sĂla Ăşchopu, pomalĂĄ rychlost chĹŻze a snĂĹženĂĄ fyzickĂĄ aktivita. Prevalence kĹehkosti 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 ohlednÄ zĂĄvislosti pacienta na okolĂ, znalost zvlĂĄdĂĄnĂ bÄĹžnĂ˝ch dennĂch aktivit a ĂşrovnÄ fyzickĂŠ zdatnosti. Existuje ĹĄirokĂĄ ĹĄkĂĄla dalĹĄĂch nĂĄstrojĹŻ k hodnocenĂ. Syndrom kĹehkosti je nezĂĄvislĂ˝ rizikovĂ˝ faktor zvýťenĂŠ morbidity, mortality a prodlouĹženĂŠ doby hospitalizace po kardiochirurgickĂŠ operaci. Tito pacienti majĂ vysokĂŠ riziko neĂşspÄchu zvolenĂŠho terapeutickĂŠho postupu. ZĂĄkladem pĂŠÄe o rizikovĂŠ pacienty je screening a prevence vzniku syndromu kĹehkosti. DĂĄ se mu pĹedejĂt dostateÄnou fyzickou aktivitou, zdravĂ˝m ĹživotnĂm stylem a pravidelnĂ˝m kognitivnĂm trĂŠninkem. © 2023, ÄKS.
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 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".