Chronic obstructive pulmonary disease in patients with end‐stage kidney disease on hemodialysis
Bibliographic record
Abstract
The objectives of this study were to assess the prevalence of chronic obstructive pulmonary disease (COPD) in hemodialysis patients with spirometry and to examine the effects of fluid removal by hemodialysis on lung volumes. Patients ≥18 years at two Danish hemodialysis centers were included. Forced expiratory volume in one second (FEV1 ), forced vital capacity (FVC), and FEV1 /FVC ratio were measured with spirometry before and after hemodialysis. The diagnosis of COPD was based on both the GOLD criteria and the lower limit of normal criteria. There were 372 patients in treatment at the two centers, 255 patients (69%) completed spirometry before dialysis and 242 of these (65%) repeated the test after. In the initial test, 117 subjects (46%) had airflow limitation indicative of COPD with GOLD criteria and 103 subjects (40.4%) with lower limit of normal criteria; COPD was previously diagnosed in 24 patients (9%). Mean FVC and FEV1 decreased mildly after dialysis (FVC: 2.84 to 2.79 L, P < 0.01. FEV1 : 1.97 to 1.93 L, P < 0.01) Hemodialysis did not affect the FEV1 /FVC ratio or number of subjects with airflow limitation indicative of COPD (113 vs. 120, P = 0.324; n = 242). COPD is a frequent and underdiagnosed comorbidity in patients on chronic hemodialysis. Spirometry should be considered in all patients on dialysis in order to address dyspnea adequately. Hemodialysis induced a small fall in mean FEV1 and FVC, which was more pronounced in patients with little or no fluid removal, but the FEV1 /FVC ratio and the number of subjects with airflow limitation indicative of COPD were not affected by dialysis.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".