Does subjective assessment of dialyzer appearance reflect dialyzer performance in online hemodiafiltration?
Bibliographic record
Abstract
INTRODUCTION: In post-dilution online hemodiafiltration, a very thin balance subsists in preventing coagulation of the extracorporeal circuit (ECC) during treatment and bleeding in the patient, concerning dialyzer status and anticoagulation dose. The aim of this study was to assess whether there are clinical outcome differences between the visual aspect of the dialyzer's status in terms of clotted fibers at end of dialysis treatments, single-pool urea kinetic modeling (spKt/V) and substitution volume (SubsVol). METHODS: It is a multicenter, descriptive-correlational study, involving 2829 patients during April 2016. Previous training was given to the Nursing staff to evaluate and classify both the dialyzer's and the venous chamber's appearance of the ECC venous line. Registration was performed at bedside immediately after the patient disconnection. FINDINGS AND DISCUSSION: Mean age was 68.96 years (SD = 13.75), 60.8% were men. The average hematocrit was 33.91% (SD = 3.45%). The average dry weight was 68.53 kg (SD = 13.27 kg). Mean unfractioned heparin (UFH) dose was 58.13 IU/kg. Only 32.4% of the patients had a clean dialyzer at the end of treatment. 19.4% of patients finished the treatment with more than 10% of clotted fibers. Patients with no residual blood (clean, 32.4%) presented a higher UFH dose (66.32 IU/kg) compared to overall average dose. UFH dose had a significant effect on dialyzer status. There were significant differences in average of spKt/V and SubsVol between the category clean and the other categories of dialyzer's status. Evaluating the dialyzer status represents an excellent opportunity to help the physicians to establish an ideal heparin dose. Only the category clean is significant to achieve the target. The nursing staff, by classifying the ECC appearance at patient's bedside and recording it in a centralized database, can be a major contributor to achieve an individualized and optimal UFH dose and subsequently better patient outcomes.
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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 | 0.002 | 0.007 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".