Mehran <i>vs.</i> Mehran2 pre-procedure: which score better predicts risk of contrast-induced acute kidney injury in patients with acute coronary syndrome?
Bibliographic record
Abstract
Background: Contrast-induced acute kidney injury (CI-AKI) is a significant concern during percutaneous coronary intervention (PCI) procedures. The novel Mehran 2 pre-procedural risk score, an updated version of the original Mehran score, shows promise as a predictive tool. However, its effectiveness specifically in acute coronary syndrome (ACS) patients requires further investigation. This study aims to evaluate the performance of Mehran 2 pre-procedure risk score compared to original score in predicting CI-AKI risk in acute coronary syndrome patients undergoing PCI. Material and Methods: A prospective cohort study was conducted with patients with ACS undergoing PCI, who were followed up for 90 days (December 2019-February 2021). The Mehran 2 CI-AKI risk score with pre-procedure data was compared with the original Mehran score. Receiver operating characteristic (ROC) curve and area under the ROC curve (AUC-ROC) were used to evaluate the discriminative capacity. Results: = 64) developed CI-AKI. CI-AKI outcome was associated with advanced age, arterial hypertension, chronic kidney disease, troponin T, hemodynamic instability, serum hemoglobin, serum creatinine, and higher both Mehran scores. Both scores demonstrated good agreement. The original Mehran score demonstrated superior CI-AKI stratification with higher sensitivity (85.94%) and specificity (60.16%) compared to the Mehran 2 pre-procedural score (sensitivity 50%, specificity 75%). Significant differences were observed in the discriminative performance between both scores. Conclusion: Sociodemographic, clinical, and laboratory variables were associated with CI-AKI. The original Mehran score demonstrated more consistent discriminative capacity for predicting CI-AKI risk in ACS patients undergoing PCI compared to the Mehran 2 pre-procedural score.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".