Determination of frail state and association of frailty with inflammatory markers among cardiac surgery patients in a Central European patient population
Bibliographic record
Abstract
INTRODUCTION: With the aging of the population, the screening of frail patients, especially before high-risk surgery, come to the fore. The background of the frail state is not totally clear, most likely inflammatory processes are involved in the development. METHODS: Our survey of patients over age of 65 who were on cardiac surgery were performed with Edmonton Frail Scale (EFS). Patients' demographic, perioperative data, incidence of complications and correlations of inflammatory laboratory parameters were studied with the severity of the frail state. RESULTS: On the basis of EFS, 313 patients were divided into non-frail (NF,163,52%), pre-frail (PF,89,28.5%) and frail (F,61,19.5%) groups. Number of complications in the three groups were different (NF:0.67/patient, PF:0.76/patient, F:1.08/patient). We showed significant difference between NF and F in both intensive care and hospital stay, but there was no statistical difference between the groups in hospital deaths (NF:5/163, PF:3/89, F:5/61). We also found a significant difference between NF and F patients in preoperative fibrinogen-, CRP- and white blood cell count levels. CONCLUSIONS: We first present the incidence of frailty in patients with heart surgery in a Central-European population. According to our results, inflammatory processes are likely to play a role in the development of the frail state.
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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 | 0.001 |
| 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".