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Record W4392589457 · doi:10.1016/j.gimo.2024.101373

P474: Precision child health: Integrating a consultative pharmacogenetics (PGx) program into clinical care at the Hospital for Sick Children*

2024· article· en· W4392589457 on OpenAlexaff
Iris Cohn, Olivia Moran, Kaitlin Stanley, Sierra Scodellaro, April Kennedy, Roozbeh Manshaei, Ruud H J Verstegen, Shinya Ito, Rebekah Jobling, Tamorah Lewis, Raymond H. Kim

Bibliographic record

VenueGenetics in Medicine Open · 2024
Typearticle
Languageen
FieldMedicine
TopicPharmaceutical studies and practices
Canadian institutionsHospital for Sick Children
Fundersnot available
KeywordsSick childPharmacogeneticsMedicineHealth carePediatricsGeneticsGenotypePolitical scienceBiology

Abstract

fetched live from OpenAlex

Pharmacogenetics (PGx) may identify inter-individual variability influencing drug response, efficacy, and the occurrence of adverse drug events and is a cornerstone of precision medicine. Despite these advantages, clinical integration of PGx faces challenges stemming from a lack of awareness and guidance. To address this, we introduce the establishment of a consultative PGx program within the Division of Clinical Pharmacology & Toxicology at The Hospital for Sick Children (SickKids). The clinic advises healthcare professionals on how to integrate PGx data into their daily clinical practice, thus increasing medication 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)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.937
Threshold uncertainty score0.733

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.119
GPT teacher head0.552
Teacher spread0.433 · 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 designNot applicable
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

Citations0
Published2024
Admission routes1
Has abstractyes

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