Arterial and Venous Estimation of Hemoglobin: A Correlational Study
Bibliographic record
Abstract
Background: The objective of this study was to identify the correlation between arterial blood gas (ABG) hemoglobin and venous hemoglobin in acutely ill patients. Methods: This was a prospective observational study on ABG and venous hemoglobin in samples drawn from 103 (59 males and 44 females) patients who presented to acute care areas like emergency room and intensive care units of a quaternary care center. ABG hemoglobin was estimated from point of care testing and venous hemoglobin was obtained from the sample sent to the laboratory. The data were entered into a specifically designed database and analyzed statistically by statistical package for the social sciences (SPSS) version 20. Results: Although there were some serious pathological involvement and associated comorbidities, statistical analysis on correlation between ABG and venous hemoglobin showed a strong positive correlation between ABG and venous hemoglobin (P < 0.01). Conclusion: ABG analysis of hemoglobin could be considered as an alternate tool for hemoglobin quantification, except in rarest scenarios that require a precise estimation of hemoglobin concentration. We also conclude that rapid estimation of hemoglobin by ABG analysis would enhance diagnostic approaches and prognostic aspects of critically ill patients. J Hematol. 2015;4(3):187-192 doi: http://dx.doi.org/10.14740/jh224e
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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".