Evidence map›Paper›PMID 36411792›Full record

ReviewERJ open research2022

COVID-19 therapies for inpatients: a review and quality assessment of clinical guidelines.

David A Wohl, Aprille A Espinueva, Lauren Dau, Chen-Yu Wang, Alexandra Lachmann, Rujuta A Bam, Aaditya Rawal, Kerris Chappell-Smith, Juergen K Rockstroh

Abstract readReview
In one paragraph

Review in ERJ open research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed, 1 pooled it
–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 synthesis or guideline pooled it.

  1. Guideline
  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

9 authors.

David A WohlInstitute of Global Health and Infectious Diseases, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
Aprille A EspinuevaGilead Sciences Inc., Foster City, CA, USA.
Lauren DauGilead Sciences Inc., Foster City, CA, USA.
Chen-Yu WangGilead Sciences Inc., Foster City, CA, USA.
Alexandra LachmannGilead Sciences Inc., Foster City, CA, USA.
Rujuta A BamGilead Sciences Inc., Foster City, CA, USA.
Aaditya RawalCostello Medical, Boston, MA, USA.
Kerris Chappell-SmithCostello Medical, Cambridge, UK.
Juergen K RockstrohDepartment of Internal Medicine, University Hospital Bonn, Bonn, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Owing to condensed development processes, expanding evidence and differences in healthcare system characteristics, many COVID-19 guidelines differ in their quality and treatment recommendations, which has consequences for clinical practice. This review aimed to identify COVID-19 treatment guidelines, assess their quality and summarise their recommendations. Guidelines were identified for five therapies most commonly used among inpatients with COVID-19 (remdesivir, dexamethasone, tocilizumab, baricitinib and casirivimab/imdevimab) from 11 countries. Guideline quality was assessed using the Appraisal of Guidelines for Research and Evaluation II (AGREE-II) tool. Full details of recommendations and supporting evidence were analysed for high-quality guidelines, defined as those scoring ≥50% in Domain 3 (Rigour of Development) of AGREE-II. Overall, guidelines differed substantially in their quality and, even among high-quality guidelines using the same evidence, recommendations regarding specific therapeutics varied. Potential reasons for this heterogeneity, including the availability and consistency of clinical data, visibility of trial end-points and context-specific factors, are discussed.

Identifiers

PMID36411792
PMCPMC9501649

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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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.