Impact of imatinib treatment on renal function in chronic myeloid leukaemia patients
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Recently, multiple epidemiological studies have linked imatinib with the alteration of renal function in chronic myeloid leukaemia (CML) patients. This meta-analysis aimed to summarize the impact of imatinib use on renal function in CML patients. METHODS: A systematic search was conducted on MEDLINE and Embase to identify articles assessing the impact of imatinib exposure on renal function in CML patients. The risk of bias was assessed using the Newcastle-Ottawa scale (NOS). Two authors independently performed literature-screening, risk of bias and data extraction. The risk of renal dysfunction (chronic kidney disease or acute kidney injury) among imatinib users was computed as the primary outcome of interest. The certainty of findings was assessed using the grading of recommendations assessment, development and evaluation (GRADE) criteria. RESULTS: A total of nine articles qualified for inclusion in the systematic review, of which four articles were eligible for meta-analysis. Based on the scoring on NOS, majority of the included studies were found to be of moderate risk of bias. Majority of the studies (n = 6) reported significantly (p < .05) decrease in estimated glomerular filtration rate (eGFR) after imatinib treatment. The risk of developing renal dysfunction (chronic kidney disease or acute kidney injury) was found to be significantly higher in imatinib users as compared to other tyrosine kinase inhibitor (TKI) users with a pooled relative risk of 2.70 (95% CI: 1.49-4.91). Sensitivity analysis also revealed a consistently high risk of renal dysfunction with imatinib use. GRADE criteria revealed low certainty of evidence. CONCLUSION: This meta-analysis found an increased risk of renal dysfunction in imatinib users compared to other TKI users.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 it