Re‐Evaluation of Acid‐Base Prediction Rules in Patients with Chronic Respiratory Acidosis
Bibliographic record
Abstract
RATIONALE: The prediction rules for the evaluation of the acid-base status in patients with chronic respiratory acidosis, derived primarily from an experimental canine model, suggest that complete compensation should not occur. This appears to contradict frequent observations of normal or near-normal pH levels in patients with chronic hypercapnia. METHODS: Linear regression analysis was used to estimate the relationships between arterial pH, bicarbonate and partial pressure of carbon dioxide (PCO2) from 18 separate arterial blood gas measurements in 18 clinically stable outpatients with chronic hypercapnic respiratory failure from chronic obstructive lung disease, and without clinical conditions or medications likely to cause a primary metabolic alkalosis. RESULTS: The PCO2 ranged from 45 mmHg to 77 mmHg, and pH ranged from 7.37 to 7.44. In only three of the arterial blood gas measurements were the pH values lower than 7.38. From the regression equations derived from these measurements, the pH decreased by 0.014 for each 10 mmHg increase in the PCO2, and the bicarbonate level increased by 5.1 mmol/L. These values are quite different from a decrease in pH of 0.03 and an increase in bicarbonate of 3.5 mmol/L predicted using the rules derived from the canine model. CONCLUSIONS: In patients with chronic stable hypercapnia, acid-base compensatory mechanisms appear to be more effective than would be predicted using the classic rules.
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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 | 0.004 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".