Evidence map›Paper›PMID 40053068›Full record

ReviewInternational orthopaedics2025

The clinical triad: a structured approach to diagnosing peripheral nerve compressions.

Elisabet Hagert

Abstract readReview
In one paragraph

Review in International orthopaedics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–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

5 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
  4. Article
  5. 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

1 author.

Elisabet HagertAspetar Orthopedic and Sports Medicine Hospital, Doha, Qatar. elisabet.hagert@aspetar.com.ORCID 0000-0002-6667-2593

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposePeripheral nerve compression syndromes are a common cause of pain, weakness, and functional limitations, yet they often remain underdiagnosed due to the limitations of traditional diagnostic methods such as electromyography and imaging. This article describes the clinical triad-manual muscle testing (MMT), sensory-collapse testing (SCT), and pain evaluation-as a structured, integrative approach to improving the diagnosis of nerve compressions.

methodsThis narrative review examines the anatomical basis and diagnostic application of the clinical triad across common peripheral nerve compression syndromes. The review focuses on the median, ulnar, and radial nerves in the upper extremity, as well as the peroneal nerve in the lower extremity. Each component of the triad is analyzed for its role in detecting nerve compressions, including the reliability of MMT for identifying muscle weakness patterns, the specificity of SCT as a confirmatory tool, and the role of pain assessment in localizing entrapment sites.

resultsThe clinical triad provides a structured and accessible diagnostic framework that enhances the detection of nerve compressions, even in early-stage presentations that may evade standard diagnostic tools. It demonstrates adaptability to complex cases, including double- and multiple-crush syndromes, and offers a non-invasive, cost-effective alternative to traditional diagnostic approaches.

conclusionThe clinical triad enhances diagnostic precision in peripheral nerve compression syndromes by integrating motor, sensory, and pain assessments. Its structured methodology facilitates early detection and targeted interventions, potentially improving patient outcomes while reducing reliance on invasive or resource-intensive diagnostic methods.

Indexed as

Nerve Compression SyndromesElectromyographyHumansNeurologic ExaminationPain MeasurementCarpal tunnel syndromeManual muscle testingNerve compression syndromesPeripheral NerveTriad

Identifiers

PMID40053068
PMCPMC11971183

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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.