Evidence map›Paper›PMID 37970605›Full record

ReviewAnnals of translational medicine2023

A treatment algorithm for reconstruction of soft tissue defects in the hand: a narrative review.

Panu H Nordback, Matti Hakkarainen, Simo Mattila

Abstract readReview
In one paragraph

Review in Annals of translational medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 citing papers in PubMed.

  1. Article
  2. Review
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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

3 authors.

Panu H NordbackDepartment of Hand Surgery, Bridge Hospital, Helsinki University Central Hospital and University of Helsinki, Helsinki, Finland.
Matti HakkarainenDepartment of Hand Surgery, Päijät-Häme Central Hospital, Päijät-Sote, Lahti, Finland.
Simo MattilaDepartment of Hand Surgery, Bridge Hospital, Helsinki University Central Hospital and University of Helsinki, Helsinki, Finland.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background and Objective: The hand is the highly specialized distal construct of the upper limb with complex and intricate anatomy. Soft tissue defects can compromise this and result in significant functional impairment. Choosing the optimal reconstructive modality is essential for the best functional recovery. The objective of this narrative review was to provide a treatment algorithm for soft tissue defects in the hand. Methods: Relevant literature for the topic was searched from PubMed and Cochrane Database from year 1953 up till end of November 2022. Specified searches for randomized controlled trials, systematic reviews and reviews were performed. Key Content and Findings: There are no randomized controlled trials published on this topic. There are six systematic reviews or meta-analyses published. Most of the literature still comprises on expert opinion and case reports with few previously proposed algorithms. Defect features, size, location and depth, patient related factors and available resources are the main determinants of the treatment strategy. In the hand, there are reasonable locoregional coverage options, but microsurgical options should be a routine alternative of the treatment repertoire. In the thumb and fingers, the reconstruction depends remarkably on injury extent. Conclusions: Patient factors, surgical expertise, etiology, severity and goal of reconstruction will invariably have influence on the ultimate reconstruction performed, and algorithms may significantly help the surgical planning.

Indexed as

Hand defectshand surgerymicrosurgeryplastic surgerysoft tissue reconstruction

Identifiers

PMID37970605
PMCPMC10632583

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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.