Evidence map›Paper›PMID 28396846›Full record

ArticleWorld journal of methodology2017

Towards automated calculation of evidence-based clinical scores.

Christopher A Aakre, Mikhail A Dziadzko, Vitaly Herasevich

Abstract read
In one paragraph

Article in World journal of methodology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Article
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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

3 authors.

Christopher A AakreChristopher A Aakre, Division of General Internal Medicine, Department of Internal Medicine, Mayo Clinic, Rochester, MN 55905, United States.
Mikhail A DziadzkoChristopher A Aakre, Division of General Internal Medicine, Department of Internal Medicine, Mayo Clinic, Rochester, MN 55905, United States.
Vitaly HerasevichChristopher A Aakre, Division of General Internal Medicine, Department of Internal Medicine, Mayo Clinic, Rochester, MN 55905, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

aimTo determine clinical scores important for automated calculation in the inpatient setting.

methodsA modified Delphi methodology was used to create consensus of important clinical scores for inpatient practice. A list of 176 externally validated clinical scores were identified from freely available internet-based services frequently used by clinicians. Scores were categorized based on pertinent specialty and a customized survey was created for each clinician specialty group. Clinicians were asked to rank each score based on importance of automated calculation to their clinical practice in three categories - "not important", "nice to have", or "very important". Surveys were solicited

resultsSeventy-nine divided by one hundred and forty-four (54.9%) surveys were completed and 72/144 (50%) surveys were completed by eligible respondents. Only the critical care and internal medicine specialties surpassed the 10-respondent threshold (14 respondents each). For internists, 2/110 (1.8%) of scores were "very important" and 73/110 (66.4%) were "nice to have". For intensivists, no scores were "very important" and 26/76 (34.2%) were "nice to have". Only the number of medical history (OR = 2.34; 95%CI: 1.26-4.67;

conclusionFew clinical scores were deemed "very important" for automated calculation. Future efforts towards score calculator automation should focus on technically feasible "nice to have" scores.

Indexed as

AutomationClinical decision supportClinical prediction ruleDecision support techniques

Identifiers

PMID28396846
PMCPMC5366935

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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.