Evidence map›Paper›PMID 29894492›Full record

ArticlePloS one2018

Clinical diagnosis of partial or complete anterior cruciate ligament tears using patients' history elements and physical examination tests.

Simon Décary, Michel Fallaha, Sylvain Belzile, Johanne Martel-Pelletier, Jean-Pierre Pelletier, Debbie Feldman, Marie-Pierre Sylvestre, Pascal-André Vendittoli, François Desmeules

Open access · goldAbstract readMulticenter Study
In one paragraph

Article in PloS one, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed, 2 pooled it
4.3field-weighted citation impact, top 6% 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

15 citing papers in PubMed, 2 syntheses or guidelines pooled it, 48 citations in OpenAlex.

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  15. Higher order thinking about differential diagnosis.Brazilian journal of physical therapy
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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

9 authors at 3 institutions in 1 country.

Simon DécarySchool of Rehabilitation, Faculty of Medicine, University of Montreal, Montreal, Quebec, Canada.ORCID 0000-0002-8672-5672
Michel FallahaDepartment of Surgery, Maisonneuve-Rosemont Hospital, Montreal, Quebec, Canada.
Sylvain BelzileDepartment of Surgery, Laval University Hospital Center (CHUL), Laval University, Quebec, Quebec, Canada.
Johanne Martel-PelletierOsteoarthritis Research Unit, University of Montreal Hospital Research Center (CRCHUM), Montreal, Quebec, Canada.
Jean-Pierre PelletierOsteoarthritis Research Unit, University of Montreal Hospital Research Center (CRCHUM), Montreal, Quebec, Canada.
Debbie FeldmanSchool of Rehabilitation, Faculty of Medicine, University of Montreal, Montreal, Quebec, Canada.
Marie-Pierre SylvestreUniversity of Montreal Hospital Research Center (CRCHUM), Montreal, Quebec, Canada.
Pascal-André VendittoliOrthopaedic Clinical Research Unit, Maisonneuve-Rosemont Hospital Research Center, Montreal, Quebec, Canada.
François DesmeulesSchool of Rehabilitation, Faculty of Medicine, University of Montreal, Montreal, Quebec, Canada.
Hôpital Maisonneuve-Rosemont · CAUniversité de Montréal · CAUniversité Laval · CA

Funding

Canadian Institute of Health Research 349856
6 · The paper itself

Abstract

objectiveTo assess the diagnostic validity of clusters combining history elements and physical examination tests to diagnose partial or complete anterior cruciate ligament (ACL) tears.

designProspective diagnostic study. SETTINGS: Orthopaedic clinics (n = 2), family medicine clinics (n = 2) and community-dwelling.

participantsConsecutive patients with a knee complaint (n = 279) and consulting one of the participating orthopaedic surgeons (n = 3) or sport medicine physicians (n = 2).

interventionsNot applicable.

main outcome measuresHistory elements and physical examination tests performed independently were compared to the reference standard: an expert physicians' composite diagnosis including history elements, physical tests and confirmatory magnetic resonance imaging. Penalized logistic regression (LASSO) was used to identify history elements and physical examination tests associated with the diagnosis of ACL tear and recursive partitioning was used to develop diagnostic clusters. Diagnostic accuracy measures including sensitivity (Se), specificity (Sp), predictive values and positive and negative likelihood ratios (LR+/-) with associated 95% confidence intervals (CI) were calculated.

resultsForty-three individuals received a diagnosis of partial or complete ACL tear (15.4% of total cohort). The Lachman test alone was able to diagnose partial or complete ACL tears (LR+: 38.4; 95%CI: 16.0-92.5). Combining a history of trauma during a pivot with a "popping" sensation also reached a high diagnostic validity for partial or complete tears (LR+: 9.8; 95%CI: 5.6-17.3). Combining a history of trauma during a pivot, immediate effusion after trauma and a positive Lachman test was able to identify individuals with a complete ACL tear (LR+: 17.5; 95%CI: 9.8-31.5). Finally, combining a negative history of pivot or a negative popping sensation during trauma with a negative Lachman or pivot shift test was able to exclude both partial or complete ACL tears (LR-: 0.08; 95%CI: 0.03-0.24).

conclusionDiagnostic clusters combining history elements and physical examination tests can support the differential diagnosis of ACL tears compared to various knee disorders.

Indexed as

Activities of Daily LivingMedical History TakingAdolescentAdultAnterior Cruciate Ligament InjuriesChildChild, PreschoolFemaleHumansInfantMaleMiddle AgedPhysical ExaminationProspective Studies

Identifiers

PMID29894492
PMCPMC5997333
OpenAlexW2808232403

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.