An improved approach to the patient with metabolic acidosis: a need for four amendments
Bibliographic record
Abstract
Clinicians should identify life-threatening issues in patients with metabolic acidosis. These threats may be present before therapy begins and/or anticipated after therapy commences. By adding four amendments, short-comings in the commonly used clinical approaches for the diagnosis of metabolic acidosis can be overcome. First, a definition of metabolic acidosis should consider not only the concentration of bicarbonate but also the content of bicarbonate in the extra cellular fluid compartment. The latter requires a quantitative estimate of the ECF volume, which can be obtained using the hematocrit and/or the total protein concentration in plasma. Second, to determine if the basis for metabolic acidosis was the addition of acids or the loss of NaHCO 3 , one must hunt for new anions, not only in plasma, but also in the urine. Third, it is important to measure the venous as well as the arterial PCO2 to assess the capacity to buffer H+ while minimizing H + binding to intracellular proteins. Fourth, to assess the role of the kidney in a patient with metabolic acidosis, the urine osmolal gap and the concentration of creatinine in the urine should be measured to provide an estimate of the rate of excretion of ammonium.
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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 | 0.008 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.018 | 0.022 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".