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Record W2607978513 · doi:10.1016/j.phrs.2017.04.027

Age and sex in drug development and testing for adults

2017· review· en· W2607978513 on OpenAlexaff
Cara Tannenbaum, Danielle E. Day

Bibliographic record

VenuePharmacological Research · 2017
Typereview
Languageen
FieldMedicine
TopicSex and Gender in Healthcare
Canadian institutionsCanadian Institutes of Health ResearchInstitute of Gender and HealthUniversité de MontréalInstitut Universitaire de Gériatrie de Montréal
Fundersnot available
KeywordsDrugClinical trialDrug developmentMedicinePharmacologyInternal medicine

Abstract

fetched live from OpenAlex

Individualization of drug therapy requires that the right drug be administered at the correct dose to patients who are likely to achieve the highest benefit and lowest risk. Female sex and age comprise two important risk factors for altered drug exposure and response. This review summarizes the current state of science for considering age and sex-related factors along the drug development pipeline, from cell culture and animal research through all phases of clinical trials in humans. A set of recommendations is provided to improve standards for integrating age and sex into the study design, analysis, and reporting of pre-clinical and clinical assessment of new molecular entities and biologics in adults.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.006
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0060.002

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 head0.834
GPT teacher head0.658
Teacher spread0.175 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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

Citations86
Published2017
Admission routes1
Has abstractyes

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