Using noninvasive hemoglobin measurements to estimate measured hemoglobin in a pediatric hemodialysis unit
Bibliographic record
Abstract
Maintaining a dialysis patient's hemoglobin (Hgb) within a very narrow range can be challenging. Relying on Hgb measurements only once or twice a month can cause large fluctuations in their measurements. Utilizing the Hgb measurement from noninvasive modalities has been studied in adult populations. Our study focused on a pediatric hemodialysis population where blood volumes are much smaller to see if these measurements would adequately work for adjusting erythropoietin dosages. We reviewed our patients' data over a 6-month time period and collected simultaneous measurements of Hgb performed in the laboratory, as well as the initial Crit-Line measurement. We then analyzed the correlation of the two estimates of the patient's Hgb using linear regression as well as Bland-Altman plot and ROCs. There were 407 simultaneous measurements of Hgb in our 32 pediatric hemodialysis patients during this time. Linear regression showed good correlation with an R value of 0.85 (P value < 0.0001). The Bland-Altman plot showed excellent agreement between the two methods. The ROC analysis showed that the performance of the noninvasive Hgb measurement was very good at predicting low measurements. Predicting Hgb less than 10 g/dL had an area under the curve of 0.94. Predicting Hgb greater than 12 g/dL had an area under the curve of 0.91. There were 100 simultaneous measurements of hematocrit. The analysis revealed similar results as the hemoglobin. Noninvasive in-line monitoring of Hgb can be a very useful way of assessing the patient's response to erythropoietin on a day-to-day time frame. Utilizing this methodology should help reduce the variability in the pediatric patients' Hgb measurements.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".