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Record W1969563181 · doi:10.3389/fgene.2014.00078

Pharmacogenomics and adverse drug reactions in children

2014· review· en· W1969563181 on OpenAlex
Michael Rieder, Bruce Carleton

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueFrontiers in Genetics · 2014
Typereview
Languageen
FieldMedicine
TopicPharmaceutical studies and practices
Canadian institutionsBC Children's HospitalWestern University
Fundersnot available
KeywordsPharmacogenomicsMedicineDrugAdverse effectIntensive care medicineDrug responsePharmacogeneticsPharmacologyBiologyGenetics

Abstract

fetched live from OpenAlex

Adverse drug reactions are a common and important complication of drug therapy in children. Over the past decade it has become increasingly apparent that genetically controlled variations in drug disposition and response are important determinants of adverse events for many important adverse events associated with drug therapy in children. While this research has been difficult to conduct over the past decade technical and ethical evolution has greatly facilitated the ability of investigators to conduct pharmacogenomic studies in children. Some of this research has already resulted in changes in public policy and clinical practice, for example in the case of codeine use by mothers and children. It is likely that the use of pharmacogenomics to enhance drug safety will first be realized among selected groups of children with high rates of drug use such as children with cancer, but it also likely that this research will be extended to other groups of children who have high rates of drug utilization and as well as providing insights into the mechanisms and pathophysiology of adverse drug reactions in children.

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.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.989
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
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 head0.042
GPT teacher head0.379
Teacher spread0.337 · 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