Evidence map›Paper›PMID 39050591›Full record

ArticleJournal of experimental orthopaedics2024

The accuracy of preoperative implant size prediction achieved by digital templating in total knee arthroplasty is not affected by the quality of lateral knee radiographs.

Lorenz Pichler, Leonhard Klein, Carsten F Perka, Clemens Gwinner, Moses K D El Kayali

Abstract read
In one paragraph

Article in Journal of experimental orthopaedics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

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0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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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

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3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Observational
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4 · The record

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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

5 authors.

Lorenz PichlerCharité-Universitätsmedizin Berlin Centrum für Muskuloskeletale Chirurgie Berlin Germany.ORCID 0000-0002-9848-778X
Leonhard KleinCharité-Universitätsmedizin Berlin Centrum für Muskuloskeletale Chirurgie Berlin Germany.
Carsten F PerkaCharité-Universitätsmedizin Berlin Centrum für Muskuloskeletale Chirurgie Berlin Germany.ORCID 0000-0002-0993-581X
Clemens GwinnerCharité-Universitätsmedizin Berlin Centrum für Muskuloskeletale Chirurgie Berlin Germany.ORCID 0000-0001-7489-245X
Moses K D El KayaliCharité-Universitätsmedizin Berlin Centrum für Muskuloskeletale Chirurgie Berlin Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Digital templating software can be used for preoperative implant size prediction in total knee arthroplasty (TKA). However, the accuracy of its prediction is reported to be low, and the impact of radiograph quality is unclear. Purpose: To investigate on the application of lateral knee radiograph quality criteria for knee rotation (KR) and knee abduction/adduction (KA) and their impact on the accuracy of final implant size prediction achieved by preoperative digital templating for TKA. Methods: A total of 191 radiographs of patients undergoing TKA were allocated into four groups according to their KR as measured at the posterior femoral condyles and their KA as measured at the distal femoral condyles on lateral knee radiographs: group A (KR ≤ 5 mm, KA ≤ 5 mm), B1 (KR > 5 mm, KA ≤ 5 mm), B2 (KR ≤ 5 mm, KA > 5 mm) and B3 (KR > 5 mm, KA > 5 mm). Preoperative templating of femoral and tibial implant size using digital templating software was carried out by two observers. Correlation coefficients (CCs) between planned and final implant size, percentage of cases with planned to final size match as well as percentage of cases within ±1 and ±2 of planned to final size were reported according to groups. Results: Group A showed the highest percentage of cases with matching planned to final femoral implant size (45%) and the highest percentage of cases with ±1 planned to final implant size (86%) as compared to B1 (match 28%, ±1 84%), B2 (match 41%, ±1 84%) and B3 (match 35%, ±1 78%). CCs for planned to final implant size were reported at >0.75 in all groups. No statistically significant difference in the CCs of planned to final implant size amongst groups was found. Conclusion: The accuracy of implant size prediction achieved by preoperative digital templating for TKA is neither affected by KR nor KA on lateral knee radiographs. Level of evidence: Level III.

Indexed as

digital templatingknee osteoarthritistotal knee arthroplasty

Identifiers

PMID39050591
PMCPMC11267166

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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.