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Record W2893167959 · doi:10.1002/jcph.1115

Adverse Drug Reactions Across the Age Continuum: Epidemiology, Diagnostic Challenges, Prevention, and Treatments

2018· review· en· W2893167959 on OpenAlex
Michael Rieder

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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueThe Journal of Clinical Pharmacology · 2018
Typereview
Languageen
FieldMedicine
TopicPharmaceutical studies and practices
Canadian institutionsWestern University
FundersCanadian Institutes of Health Research
KeywordsMedicinePharmacogenomicsDosingDrug reactionEpidemiologyDrugIntensive care medicinePharmacotherapyPediatricsPharmacovigilanceAdverse effectPharmacologyInternal medicine

Abstract

fetched live from OpenAlex

Adverse drug reactions (ADRs) are common and important complications of drug therapy for children. The risk for ADRs changes over childhood, as do the nature and types of ADRs. Importantly, the risk and nature of ADRs in children are markedly different from those of adults, and adult data cannot be relied on to guide safe drug therapy in children. There are groups of children, notably those with complex and chronic diseases, who are at substantial risk for ADRs. The evaluation of an undesired effect during therapy is ideally accomplished by an organized approach that is a skill that clinicians who care for children-especially those children at high risk for ADRs must have. Additionally, clinicians as well as drug regulatory agencies and industry need to be both vigilant and astute as well as aware that ADRs in children are often different in nature and frequency from those in adults. The increasing use of pharmacogenomics to guide drug dosing and the increasing number of biological agents will provide new sets of challenges to clinicians over the next decade.

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)0.017
metaresearch head score (Gemma)0.012
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.837
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0170.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0000.000
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.003
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 head0.483
GPT teacher head0.626
Teacher spread0.144 · 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