Bibliographic record
Abstract
OBJECTIVE: To review rules and formulas for solving even the most complex acid-base problems. SOURCES OF INFORMATION: MEDLINE was searched from January 1966 to December 2003. The search was limited to English-language review articles involving human subjects. Nine relevant review papers were found and provide th background. As this information is well established and widely accepted, it is not judged for strength of evidence, a is standard practice. MAIN MESSAGE: An understanding of the body's responses to acidemia or alkalemia can be gained through a set of four rules and two formulas that can be used to interpret almost any acid-base problems. Physicians should, however, remember the "golden rule" of acid-base interpretation: always look at a patient's clinical condition. CONCLUSION: Physicians practising in acute care settings commonly encounter acid-base disturbances. While some o these are relatively simple and easy to interpret, some are more complex. Even complex cases can be resolved usin the four rules and two formulas.
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 | 0.026 | 0.099 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.012 | 0.006 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.012 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".