Proto-dialytic cardiac function relates to intra-dialytic morbid events
Bibliographic record
Abstract
BACKGROUND: Intra-dialytic morbid events (IDME) such as intra-dialytic hypotension (IDH) and muscle cramps frequently complicate haemodialysis (HD). Cardiac dysfunction is highly prevalent in HD patients. We investigated the relationship between proto-dialytic (i.e. early intra-dialytic) cardiac function and IDME in HD patients. METHODS: Heart rate, beat-to-beat blood pressure (BP) and cardiac output were continuously measured during the first 30 min of dialysis treatment using the Task Force™ Monitor. Total peripheral resistance index (TPRI) was calculated from cardiac index (CI) and BP. Univariate, multivariate and logistic regression analyses were employed to relate IDME to haemodynamic predictors; Kaplan-Meier method was employed for time-to-event analysis. RESULTS: Fourteen HD patients (age 67 ± 15 years; 7 females) were studied. Dialysis treatment was complicated by IDH and muscle cramps in 4 and 8 out of 30 sessions, respectively. CI was higher in patients without IDME (2.6 ± 0.5 L/min/m(2)) as compared to those with muscle cramps (2.0 ± 0.3 L/min/m(2)) or IDH (1.8 ± 0.2 L/min/m(2); all P < 0.05). CI and TPRI at baseline independently predicted IDME in a multivariate regression analysis (odds ratio: 0.043 per unit of CI, 95% confidence interval: 0.003-0.611; odds ratio: 1.124 per unit of TPRI, 95% confidence interval: 1.25-1.01). Patients were stratified by tertiles of CI. IDME occurred in the two lower tertiles, whereas patients in the upper tertile were event free (log-rank test, P < 0.002). CONCLUSIONS: Low CI and high TPRI in the first 30 min of HD are associated with an increased risk of IDME.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| 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".