Bibliographic record
Abstract
Polypharmacy is common in hemodialysis patients. The objective of this study is to identify drug-related problems (DRPs) in hemodialysis patients, intervene, and resolve them. All patients undergoing dialysis at a hemodialysis center were enrolled into the study. Patients who had been hospitalized during the study period were excluded. DRPs were identified after thorough review of the patients’ medication and clinical records. DRPs were classified into 8 categories and any DRP that did not fit into the 8 categories was classified under ‘Others.’ Appropriate recommendations for the resolution of the DRPs were presented to the nephrologist in-charge of the center and action taken. Accepted recommendations were deemed as interventions and assigned a significance rank on a scale of 1 (adverse significance) to 6 (extreme significance). Where recommendations were accepted, monitoring was carried out 2 weeks later to assess the clinical outcome of the intervention. A total of 35 patients were studied. 31 patients completed the study, 4 were lost to follow-up. In a 3-month period, 83 DRPs were identified and 73 interventions (88%) made. A mean of 2.7 ± 1.1 DRPs were detected per patient. Drug underdose constituted the most common DRP accounting for 35% of all DRPs. 62% of the accepted recommendations were classified as significant and given a rank of 4/6. On follow-up, 54% of the interventions showed improved clinical outcomes. DRPs are prevalent in hemodialysis patients. The introduction of clinical pharmacy services can potentially contribute to many aspects of healthcare in hemodialysis patients through the detection and resolution of DRPs. Where clinical pharmacy services are not available, clinicians should be vigilant regarding polypharamcy and the occurrence of DRPs.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".