Evidence map›Paper›PMID 29419670›Full record

ArticleMedicine2018

Improved detectability of thromboses of the lower limb using low kilovoltage computed tomography.

Tomoaki Sasaki, Yasuomi Fujimoto, Shunta Ishitoya, Basim Nabaa, Naofumi Watanabe, Toshihiro Yamaki, Koji Takahashi

Open access · goldAbstract read
In one paragraph

Article in Medicine, 2018. 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
0.6field-weighted citation impact, top 31% 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, 5 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

7 authors at 1 institution in 1 country.

Tomoaki SasakiDepartment of Radiology, Asahikawa Medical University, Asahikawa, Hokkaido, Japan.
Yasuomi Fujimoto
Shunta Ishitoya
Basim Nabaa
Naofumi Watanabe
Toshihiro Yamaki
Koji Takahashi
Asahikawa Medical University · JP

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

To determine the utility of low kilovoltage computed tomographic venography (CTV) for the detection of deep venous thrombus in the lower limbs.Twenty-one thrombi in 19 enrolled patients were investigated in this retrospective study. Patients were initially scanned using CTV at 100 kVp, at the femur level, followed by an immediate switch to 80 kVp. We assessed the CT values of thrombi and veins and performed subjective evaluation for detecting thrombi using a 5-point scoring system: 1, unable to evaluate due to noise or artifacts; 2, equivocal venous thrombus; 3, possible venous thrombus; 4, probable venous thrombus; and 5, definite venous thrombus.Venous density on 100-kVp images (mean ± SD [standard deviation]: 122 ± 23 HU, 95% confidence interval [CI]: 111-133 Hounsfield unit [HU]) was significantly lower than that on 80-kVp images (136 ± 24 HU, 95% CI: 125-147 HU, P < .001). There was no significant difference in thrombi between 100-kVp images (55 ± 14 HU, 95% CI: 49-61 HU) and 80-kVp images (57 ± 16, 95% CI: 50-64 HU, P = .168). The thrombus to vein ratio on 100-kVp images (0.47 ± 0.20, 95% CI: 0.39-0.56) was significantly higher than that on 80-kVp images (0.44 ± 0.16, 95% CI: 0.37-0.51, P = .048). The mean 5-point score was significantly higher on the 80-kVp images (4.76) than on the 100-kVp images (4.45, P = .016).Lower kilovoltage CTV significantly improved thrombotic to venous contrasts in the lower limbs.

Indexed as

AgedFemaleHumansJapanLower ExtremityMaleMiddle AgedPhlebographyRadiographic Image EnhancementRetrospective StudiesThrombosisTomography, X-Ray ComputedVenous Thrombosis

Identifiers

PMID29419670
PMCPMC5944700
OpenAlexW2790410517

What OpenQuestion holds

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LicenceCC BY-NC-SA
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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.