Evidence map›Paper›PMID 36644063›Full record

ArticleCureus2022

Using Machine Learning Technique to Predict the Most Reliable Diagnostic Finding for Foreign Body Aspiration in Children: Symptoms, Chest X-ray, or Auscultation?

Incinur Genisol, Osman Uzunlu

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. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
0.1field-weighted citation impact, top 50% 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

0 citing papers in PubMed, 1 citations in OpenAlex.

No citing paper in PubMed yet.

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

2 authors at 1 institution in 1 country.

Incinur GenisolPediatric Surgery, Sisli Hamidiye Etfal Training And Research Hospital, Istanbul, TUR.
Osman UzunluPediatric Surgery, Pamukkale University School of Medicine, Denizli, TUR.
Pamukkale University · TR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Foreign body aspiration (FBA) is one of the most critical and life-threatening pediatric emergency situations. Prompt diagnosis in these cases is very important as they are associated with high mortality among children. When diagnosing FBA, symptoms of the patient, auscultation findings, and chest X-ray findings are usually evaluated. In this study, we conducted a retrospective analysis of all the cases involving suspicion of FBA in children under the age of 18 years who were hospitalized in the Department of Pediatric Surgery at Denizli Pamukkale University Hospital, Turkey from January 2005 to September 2020. Instead of traditional statistical methods, we used machine learning techniques such as random forest and logistic regression to determine which finding was diagnostically the most reliable. The variables included in the analysis that were considered to be significant were as follows: symptoms, auscultation findings, chest X-ray findings, patient gender, age, location of the foreign body, and the time of admission. For the purpose of this study, we developed four different models. Model 1 included gender, age, time of admission, location, and symptoms as variables; the correct classification rate of the model was found to be 82.3%. Model 2 included auscultation findings in addition to Model 1, and the correct classification rate of the model was 84.8%. Model 3 included chest X-ray findings in addition to Model 1, and the correct classification rate of the model was 87.4%. Model 4, on the other hand, included both auscultation findings and chest X-ray findings in addition to Model 1, and the correct classification rate of the model was 87.6%. Based on our findings, a definitive diagnosis of FBA using only symptoms, auscultation findings, or chest X-ray findings in isolation does not seem possible. Additionally, using only symptoms and chest X-ray findings is also insufficient to make a diagnosis.

Indexed as

bronchoscopychildrenforeign body aspirationmachine learning techniquepediatric emergency

Identifiers

PMID36644063
PMCPMC9834759
OpenAlexW4311397045

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.