Evaluation of palliative care patients with different scales in the emergency department and the importance of home healthcare
Bibliographic record
Abstract
Introduction:With this comprehensive study, we aimed to contribute to the integration process of palliative care (PC) to emergency departments (ED) by determining ED patients needing PC, with the help of a new screening method and assessment with the Screen for Palliative and Endof-Life Care Needs in the emergency department (SPEED), Karnofsky performance scale (KPS), and Edmonton Symptom Assessment Scale (ESAS). Material and methods:Patients who were admitted to the ED between 2015 and 2017 were included in this prospective study.The study form included the following variables: demographic information, duration of diagnosis, PC follow-up, consultation status, and the outcome in ED.SPEED, KPS, and ESAS were applied to the patients. Results:The study was carried out with the participation of 150 patients.The mean score of the patients on the KPS was 43.13.The most common symptoms observed in patients were fatigue, pain, anorexia, and nausea, respectively.It was determined that patients who did not receive home healthcare were more likely to feel tired, sad, and anxious, and the SPEED levels of these patients were found to be higher.Conclusions: The present study is the first to identify the group of ED patients requiring PC and to determine and accordingly evaluate the current state and symptoms of this patient group using scales.Accordingly, it would be a correct approach to apply ESAS and KPS to patients in order to better evaluate the symptoms present in ED.At the same time, it was determined that home healthcare services play an important role in PC.
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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 | 0.003 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".