Evidence map›Paper›PMID 36060541›Full record

ReviewFrontiers in digital health2022

Timing errors and temporal uncertainty in clinical databases-A narrative review.

Andrew J Goodwin, Danny Eytan, William Dixon, Sebastian D Goodfellow, Zakary Doherty, Robert W Greer, Alistair McEwan, Mark Tracy, Peter C Laussen, Azadeh Assadi and 1 more

Abstract readReview
In one paragraph

Review in Frontiers in digital health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. 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

11 authors.

Andrew J GoodwinDepartment of Critical Care Medicine, The Hospital for Sick Children, Toronto, ON, Canada.
Danny EytanDepartment of Critical Care Medicine, The Hospital for Sick Children, Toronto, ON, Canada.
William DixonDepartment of Critical Care Medicine, The Hospital for Sick Children, Toronto, ON, Canada.
Sebastian D GoodfellowDepartment of Critical Care Medicine, The Hospital for Sick Children, Toronto, ON, Canada.
Zakary DohertyResearch Fellow, School of Rural Health, Monash University, Melbourne, VIC, Australia.
Robert W GreerDepartment of Critical Care Medicine, The Hospital for Sick Children, Toronto, ON, Canada.
Alistair McEwanSchool of Biomedical Engineering, University of Sydney, Sydney, NSW, Australia.
Mark TracyNeonatal Intensive Care Unit, Westmead Hospital, Sydney, NSW, Australia.
Peter C LaussenDepartment of Anesthesia, Boston Children's Hospital, Boston, MA, United States.
Azadeh AssadiDepartment of Critical Care Medicine, The Hospital for Sick Children, Toronto, ON, Canada.
Mjaye MazwiDepartment of Critical Care Medicine, The Hospital for Sick Children, Toronto, ON, Canada.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

A firm concept of time is essential for establishing causality in a clinical setting. Review of critical incidents and generation of study hypotheses require a robust understanding of the sequence of events but conducting such work can be problematic when timestamps are recorded by independent and unsynchronized clocks. Most clinical models implicitly assume that timestamps have been measured accurately and precisely, but this custom will need to be re-evaluated if our algorithms and models are to make meaningful use of higher frequency physiological data sources. In this narrative review we explore factors that can result in timestamps being erroneously recorded in a clinical setting, with particular focus on systems that may be present in a critical care unit. We discuss how clocks, medical devices, data storage systems, algorithmic effects, human factors, and other external systems may affect the accuracy and precision of recorded timestamps. The concept of temporal uncertainty is introduced, and a holistic approach to timing accuracy, precision, and uncertainty is proposed. This quantitative approach to modeling temporal uncertainty provides a basis to achieve enhanced model generalizability and improved analytical outcomes.

Indexed as

clinicalclockserrorsICUmedicinemetrologytimeuncertainty

Identifiers

PMID36060541
PMCPMC9433547

What Socratic holds

Textmetadata
LicenceCC BY
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 Socratic graph.