65 Yaş ve Üzeri Hematolojik Onkoloji Hastalarının Kırılganlık Düzeylerinin Belirlenmesi
Bibliographic record
Abstract
This descriptive study aimed to determine the frailty levels of geriatric hematologic oncology patients. Sample of study was 90 patients aged 65 and older, diagnosed with hematologic oncology disorder, hospitalized in hematologic oncology clinic and admitted to outpatient clinic at an education and research hospital in Ankara, Turkey. Patient data sheet and Edmonton Frailty Scale were used for data collection. Data were analyzed with the Mann Whitney-U test, Kruskal Wallis, Ki Square. 60% of patients were frail and mean score of scale was 5,59±3,13 (max. 11). Patients’ frailty level distributions were not frail %40, vulnerable %17,8, mild frailty %20, moderate frailty %16,7, and severe frailty %5,5. Frailty score was higher in patients aged 75 and over, had 4 children and over, diagnosed with leukemia and diagnosis duration lasted 2 years or longer. There was no statistically significant difference between gender, marital status, socioeconomic status, comorbidity, and frailty level. With this study geriatric hematologic oncology patients’ frailty levels were determined. According to the results, treatment plan, and nursing care should be planned based on frailty levels and risk of patients.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.003 | 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".