Evidence map›Paper›PMID 36627984›Full record

ArticleCureus2022

Hospital Practices for Respiratory Isolation for Patients With Suspected Tuberculosis and Potential Application of Prediction Models.

Joshua Davidson, Bryan Chesen, Samir Kumar, Daniel J Shayowitz

Open access · diamondAbstract read
In one paragraph

Article in Cureus, 2022. 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
0.2field-weighted citation impact, top 47% of its field
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, 2 citations in OpenAlex.

  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

4 authors at 2 institutions in 1 country.

Joshua DavidsonPulmonary and Critical Care Medicine, BronxCare Health System, New York, USA.
Bryan ChesenPulmonary and Critical Care Medicine, State University of New York Downstate Health Sciences University, Brooklyn, USA.
Samir KumarPulmonary and Critical Care Medicine, Mount Sinai Health System, New York, USA.
Daniel J ShayowitzInternal Medicine, State University of New York Downstate Health Sciences University, Brooklyn, USA.
Mount Sinai Health System · USState University of New York · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Hospitalized persons with suspected pulmonary tuberculosis (PTB) are placed in airborne isolation to prevent nosocomial infection, as recommended by the Centers for Disease Control and Prevention (CDC). There is significant evidence that clinicians overuse this resource due to an abundance of caution when confronted with a patient with possible PTB. Many researchers have developed predictive tools based on clinical and radiographic data to assist clinicians in deciding which patients to place in respiratory isolation. We assessed the isolation practices for an urban hospital serving a large immigrant population and then retrospectively applied seven previously derived prediction models of isolation of PTB to our population. Our current clinical practice results in 76% of patients with PTB being placed in isolation on admission. However, 208 patients without PTB were placed in isolation unnecessarily for a total of 584 days. Four models had sensitivities greater than 90%, and two models had sensitivities of 100%. The use of these models would have potentially saved more than 150 days of patient isolation per year.

Indexed as

active pulmonary tuberculosisairborne infection controlairborne isolationdecision treeflexible fiberoptic bronchoscopy (ffb)hospital infection controlhospital isolationprediction tools: tuberculosis

Identifiers

PMID36627984
PMCPMC9822524
OpenAlexW4311830414

What OpenQuestion 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 OpenQuestion graph.