Evidence map›Paper›PMID 37561265›Full record

ArticleJournal of pharmacokinetics and pharmacodynamics2024

Optimal sample selection applied to information rich, dense data.

David Wang, Tak Hung, Noelyn Hung, Paul Glue, Chris Jackson, Stephen Duffull

Abstract readLetter
PubMed Publisher
In one paragraph

Article in Journal of pharmacokinetics and pharmacodynamics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

David WangDepartment of Anaesthesia, Waikato Hospital, Hamilton, New Zealand. david.wang@waikatodhb.health.nz.ORCID 0000-0001-5427-4961
Tak HungZenith Technology Limited, Dunedin, New Zealand.
Noelyn HungDepartment of Pathology, University of Otago, Dunedin, New Zealand.
Paul GlueDepartment of Psychological Medicine, University of Otago, Dunedin, New Zealand.
Chris JacksonDepartment of Medicine, University of Otago, Dunedin, New Zealand.
Stephen DuffullCertara, Jersey City, New Jersey, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Dense data can be classified into superdense information-poor data (type 1 dense data) and dense information-rich data (type 2 dense data). Arbitrary, random, or optimal thinning may be applied to type 1 dense data to minimise computational burden and statistical issues (such as autocorrelation). In contrast, a prospective or retrospective optimal design can be applied to type 2 dense data to maximise information gain from limited resources (capital and/or time). Here we describe a retrospective optimal selection strategy for quantification of unbound drug concentration from a discrete set of plasma samples where the total drug concentration has been measured.

Indexed as

Prospective StudiesRetrospective StudiesDense dataOptimal designSample selection

Identifiers

PMID37561265

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.