The use of near infrared interactance in hemodialysis
Bibliographic record
Abstract
Forty‐one consecutive admissions to a hemodialysis center were evaluated. Demographic information including age, gender, race, and diagnosis was collected. Patients, >18 years old, with end stage renal disease and on hemodialysis for at least one year were included. Those with edema or known ascites were excluded. Weight was measured before and after hemodialysis (HD) using a standard scale and by considering the amount of fluid loss by the hemodialysis machine. Body composition including total body water (TBW) was calculated before and after HD using near infrared interactance (NIR). All measurements were completed during half hour before and after HD. Forty‐one patients included: men (n = 26), women (n = 15); median age 58 (range 28–88 years). Twenty‐eight were African American and the rest Caucasians. The amount of intravascular fluid taken after HD (assessed by weight reduction) ranged 0–5 L with median 2.2 L. NIR analysis for the same patients at the same time showed different total body water measurements in 91% of cases (P > 0.05). Moreover, NIR analysis showed increase in total body water in 24% of patients even though the hemodialysis machine showed a loss of total body water; median of 1.3 (range: 0–3L). The error in measuring body composition with NIR was both large and varied (random and not systematic error). We conclude that NIR analysis cannot be considered as a reliable method to evaluate body composition, especially total body water, amongst patients with end stage renal disease undergoing hemodialysis.
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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".