Prevalencia de fragilidad en cirugía electiva para personas mayores usuarios del Hospital de Clínicas Análisis de la población quirúrgica de personas mayores del Hospital de Clínicas “Dr. Manuel Quintela” (marzo 2019-marzo 2020)
Bibliographic record
Abstract
Frailty determines an inability to cope with stressors due to decreased multisystem physiologic reserves. The surgical anesthetic act is a stressful event and the presence of frailty is an independent risk factor for perioperative morbidity and mortality Detection of frailty would allow for addressing reversible factors causing it, with the intention of reducing the risks that are inherent to anesthetic acts.Detection in the perioperative assessment provides relevant information that is not obtained in a traditional evaluation. This approach has become the standard in perioperative assessment of geriatric surgical patients.The study aims to assess the prevalence of frailty in elective surgery for the elderly at Clínicas Hospital.Method: prospective, descriptive study approved by the institutional Ethics Committee. 206 patients aged 65 years old and over who had been coordinated for elective surgery were recruited for the study between March, 2019 and March, 2020. The Reported Edmonton Frailty Scale (REFS) was applied to detect frailty.Prevalence of frailty was 22.8% with a CI of 16-29 in this population, rather high and similar to the frail patients percentages in other surgical and non-surgical settings. Significantly higher numbers of arterial hypertension, arrhythmias, diabetes and hypothyroidism cases and tobacco users were found among frail patients.Prevalence and impact of frailty on operative morbidity and mortality are compelling reasons for its inclusion in the perioperative assessment of our health system, as well as the training of anesthesiologists in the detection of frailty through the use of practical, valid and reliable tools.
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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 | 0.001 |
| 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) | 0.002 | 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".