Does altitude affect blood gases in hemodialysis patients?
Bibliographic record
Abstract
Abstract Introduction This study aimed to determine whether predialysis blood gases is affected by altitude differences in hemodialysis patients with arteriovenous fistulas living in Turkey at three different altitudes. Methods Patients' predialysis blood gases were compared by standardizing both arterial blood gases collections and working methods for patients undergoing hemodialysis using a dialysate with the same properties at altitudes of 30 m (sea level), 1020 m (moderate altitude), and 1951 m (high altitude). Findings Blood gases disorders were detected in 32 (82.1%) high altitude group patients, whereas 49 (74.2%) sea level group patients had no blood gases disorders (P < 0.001). pH values in the high altitude group were significantly lower than those in the other groups, and the pH increased as altitude decreased (P < 0.001). The partial pressure of carbon dioxide (PaCO2) values was higher in the sea level group than in the other groups and increased at lower sea levels (P < 0.001). Bicarbonate values were significantly higher in the sea level group than in the other groups and increased at lower sea levels, similar to PaCO2 values (P < 0.001). The partial pressure of oxygen (PaO2) values in the high altitude and sea level groups were significantly higher and increased at lower sea levels (P < 0.001). The oxygen saturation (SaO2) values were significantly lower in the high altitude group than in the other groups and increased gradually at lower sea levels (P < 0.001). Discussion Predialysis metabolic acidosis was more pronounced in patients undergoing hemodialysis at high altitudes, whereas PaCO2, PaO2, and SaO2 values were lower.
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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 | 0.000 | 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) | 0.001 | 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".