Drug related problems in inpatients of Santiago de Cuba
Bibliographic record
Abstract
Introduction: Drug-related problems are a major health concern because of their high impact on inpatient morbidity. Method: Descriptive and cross-sectional observational, study in the clinical services of five health institutions of Santiago de Cuba, during the first quarter of 2020. The profiles of 329 patients who received pharmacotherapeutic follow-up were reviewed. The sample was characterized according to biosocials, clinical and pharmacotherapeutic variables, the identification of medication-related problems was performed using the criteria of Cipolle, Stramd and Morley, also determining the drugs involved in medication-related problems. The data were processed through absolute and relative frequencies represented by means of tables and figures. Results: Patients over or equal to 60 years of age predominated, representing 38.6 %; 61.4 % of the patients were female. Most of the patients had up to two diseases (76.3 %); complicated respiratory infections (35.6 %) were the most frequent reason for admission. Between four and six medications were consumed by 36.5 %, with antibacterials for systemic use being the most prescribed. A total of 598 drug-related problems were identified for a ratio of 1.8 DRP/patient, of which 42.8 % corresponded to safety problems, 31.1 % to indication, followed by 24.9 % to effectiveness and finally 1.2 % to adherence. Conclusions: Antimicrobials were the most implicated in the occurrence of medication-related problems. Pharmaceutical care offers services that ensure the appropriate use of medications.
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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 | 0.001 |
| 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 | 0.000 |
| 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".