Assessment of Symptom Burden Using the ESAS in Patients with Hypercapnic Respiratory Failure Receiving Noninvasive Mechanical Ventilation
Bibliographic record
Abstract
Aim: The success of noninvasive mechanical ventilation (NIV), which is used in the treatment of hypercapnic respiratory failure (HRF), largely depends on patient adherence. One of the key components of adherence is effective symptom control. Therefore, it is recommended that symptoms other than dyspnea be systematically assessed in patients requiring NIV. This study was designed to evaluate the symptom burden of such patients using the Edmonton Symptom Assessment Scale (ESAS). Material and Method: This study, conducted between January 2025 and March 2025, included patients undergoing NIV. The demographic characteristics and comorbidities of the patients were assessed. Symptoms and their severity were evaluated using the ESAS. Additionally, patients were divided into two groups: those with prior NIV experience and those undergoing NIV for the first time, and their symptom burden was analyzed accordingly. Results: The mean age of the patients included in the study was 69±12 years, with 68.4% being male. According to the ESAS results, the most frequently reported symptoms were dyspnea (6.21±1.05), overall well-being (5.65±2.49), and fatigue (5.03±1.74). When comparing symptom burden based on NIV experience, dyspnea scores were significantly higher in patients with prior NIV use (p
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 | 0.001 | 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".