Non‐invasive evaluation of blood oxygen saturation and hematocrit from <i>T</i><sub><i>1</i></sub> and <i>T</i><sub><i>2</i></sub> relaxation times: In‐vitro validation in fetal blood
Bibliographic record
Abstract
Purpose We propose an analytical method for calculating blood hematocrit (Hct) and oxygen saturation (sO2) from measurements of its T1 and T2 relaxation times. Theory Through algebraic substitution, established two‐compartment relationships describing and as a function of hematocrit and oxygen saturation were rearranged to solve for Hct and sO2 in terms of R1 and R2. Resulting solutions for Hct and sO2 are the roots of cubic polynomials. Methods Feasibility of the method was established by comparison of Hct and sO2 estimates obtained from relaxometry measurements (at 1.5 Tesla) in cord blood specimens to ground‐truth values obtained by blood gas analysis. Monte Carlo simulations were also conducted to assess the effect of T1, T2 measurement uncertainty on precision of Hct and sO2 estimates. Results Good agreement was observed between estimated and ground‐truth blood properties (bias = 0.01; 95% limits of agreement = ±0.13 for Hct and sO2). Considering the combined effects of biological variability and random measurement noise, we estimate a typical uncertainty of ±0.1 for Hct, sO2 estimates. Conclusion Results demonstrate accurate quantification of Hct and sO2 from T1 and T2. This method is applicable to noninvasive fetal vessel oximetry—an application where existing oximetry devices are unusable or require risky blood‐sampling procedures. Magn Reson Med 78:2352–2359, 2017. © 2017 International Society for Magnetic Resonance in Medicine.
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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 | 0.001 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".