Optical monitoring in kidney transplant
Bibliographic record
Abstract
The main objective of this study was to assess the feasibility of NIRS (PortaMon®, Artinis Medical Systems) as a noninvasive monitoring method for kidney transplant function. We hypothesized that changes in NIRS (near infra-red spectroscopy) parameters are associated with changes in graft function as estimated by Glomerular Filtration Rate (GFR) based on serum creatinine level. Two cohorts of participants were recruited: Immediate postoperative and those with at least 8 weeks post-transplant. Other inclusion criteria were BMI<25kg/m2 and AWD (abdominal wall depth) <2cm. We evaluated 4 patients immediately after the surgery in the operating room and continued the examination every day while they were in the hospital. We also monitored them for 12 weeks after surgery. The second group (16 patients) had their surgery in a median of 26 months prior to their recruitment. The procedure of NIRS monitoring was the same for both groups. We examined the surgical sites and opposite sides of the abdomen as our control for at least 3 minutes each time. The collected data were compared with clinical parameters (BP, e-GFR, and Resistive Index). We analyzed the collected data using linear regression model and we noted a positive significant correlation between the tissue saturation index of the surgical site (TOISS%) and e-GFR adjusted for BMI. We also examined the ∆TOI% (percentage of difference of oxygen saturation index between the surgical site and opposite site) to account for the abdominal muscle effects on collected signals.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".