A Pilot Project for Clinical Pharmacy Services in a Clinic for Children With Medical Complexity
Bibliographic record
Abstract
OBJECTIVE: The primary objective of the project was to assess the impact of clinical pharmacy services in a clinic for children with medical complexity. Secondary objectives were to identify and characterize the drug-related needs of these patients and to describe and develop the role of a pharmacist in the clinic. METHODS: This was a prospective descriptive study in which a clinical pharmacist staffed the clinic for children with medical complexity for 11 weeks, from January to March 2011. This allowed for the collection of baseline data, such as patient characteristics and measurements of pharmacist workload and assessment (eg, types of drug therapy problems, medication reconciliation, medication teaching). RESULTS: A pharmacist participated in 46 clinic visits with 43 patients, identifying a total of 42 drug therapy problems. Of the 42 problems, 35 actual and 7 potential drug problems were identified, resulting in approximately 1 problem per patient. The most common actual problems included "dose too small" (37.1%) and "patient requires a medication for untreated condition" (20%). Common potential problems included "drug interactions" (43%) and "adverse effects" (57%). CONCLUSIONS: The pilot study demonstrates that children with medical complexity are at high risk for drug therapy problems and the presence of a clinic pharmacist is beneficial in the identification, prevention, and resolution of drug therapy problems, while helping ensure continuity of care in this population.
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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 | 0.013 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".