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Record W2763374152 · doi:10.1093/pch/pxy120

Patient access to compounded drugs in paediatrics after discharge from a tertiary centre

2018· article· en· W2763374152 on OpenAlexaff
Marie-Kim Héraut, Minh-Thu Duong, Clara Elchebly, W Yu, Niina Kleiber, Stéphanie Tremblay, Marie‐Élaine Métras, Denis Lebel, Jean‐François Bussières

Bibliographic record

VenuePaediatrics & Child Health · 2018
Typearticle
Languageen
FieldMedicine
TopicPharmaceutical studies and practices
Canadian institutionsUniversité de MontréalCentre Hospitalier Universitaire Sainte-Justine
Fundersnot available
KeywordsMedicineMedical prescriptionObservational studyPediatricsPharmacyCompoundingDrugFamily medicineEmergency medicineMedical emergencyPsychiatryPharmacologyInternal medicine

Abstract

fetched live from OpenAlex

OBJECTIVE: To describe the problems faced by young patients and their parents when obtaining and using compounded drugs. METHODS: This prospective observational descriptive study included patients 0 to 21 years of age who were discharged from a mother-child tertiary hospital with a prescription containing at least one compounded drug between February 2016 and July 2016. Families were called 7 to 10 business days after discharge to complete a telephone follow-up questionnaire. Retail pharmacies were contacted to obtain additional information in order to compare the dispensed compounded drug with the prescription and published master formulas. RESULTS: The parents of 71 patients with a median age of 6.9 months were surveyed regarding 99 compounded drugs corresponding to 34 different oral formulations. Out of 314 issues identified, 252 were considered as problems: 9 involved major and 243 minor problems with real or potential consequences. CONCLUSION: This study identified a significant number of compounding-related problems. It suggests that current practice standards are insufficient and action should be taken to improve the use and the dispensation of compounded drugs to ensure patients' safety.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.183
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.026
GPT teacher head0.346
Teacher spread0.320 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2018
Admission routes1
Has abstractyes

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