Evidence map›Paper›PMID 38929810›Full record

ReviewJournal of personalized medicine2024

Robotic Total Knee Arthroplasty: An Update.

Gennaro Pipino, Alessio Giai Via, Marco Ratano, Marco Spoliti, Riccardo Maria Lanzetti, Francesco Oliva

Abstract readReview
In one paragraph

Review in Journal of personalized medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed, 1 pooled it
–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

9 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
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  5. Review
  6. Article
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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

6 authors.

Gennaro PipinoDepartment of Orthopedic Surgery and Traumatology Villa Erbosa Hospital, Gruppo San Donato, 40129 Bologna, Italy.
Alessio Giai ViaDepartment of Orthopedic Surgery and Traumatology, San Camillo-Forlanini Hospital, 00152 Rome, Italy.
Marco RatanoUnit of Orthopaedics, Department of Life, Health and Environmental Sciences, University of L'Aquila, 67100 L'Aquila, Italy.
Marco SpolitiDepartment of Orthopedic Surgery and Traumatology, San Camillo-Forlanini Hospital, 00152 Rome, Italy.
Riccardo Maria LanzettiDepartment of Orthopedic Surgery and Traumatology, San Camillo-Forlanini Hospital, 00152 Rome, Italy.
Francesco OlivaFull Professor Department of Human Sciences and Promotion of the Quality of Life, San Raffaele Roma Open University, 00166 Rome, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Total knee arthroplasty (TKA) is a gold standard surgical procedure to improve pain and restore function in patients affected by moderate-to-severe severe gonarthrosis refractory to conservative treatments. Indeed, millions of these procedures are conducted yearly worldwide, with their number expected to increase in an ageing and more demanding population. Despite the progress that has been made in optimizing surgical techniques, prosthetic designs, and durability, up to 20% of patients are dissatisfied by the procedure or still report knee pain. From this perspective, the introduction of robotic TKA (R-TKA) in the late 1990s represented a valuable instrument in performing more accurate bone cuts and improving clinical outcomes. On the other hand, prolonged operative time, increased complications, and high costs of the devices slow down the diffusion of R-TKA. The advent of newer technological devices, including those using navigation systems, has made robotic surgery in the operatory room more common since the last decade. At present, many different robots are available, representing promising solutions to avoid persistent knee pain after TKA. We hereby describe their functionality, analyze potential benefits, and hint at future perspectives in this promising field.

Indexed as

arthrosiscomputer assisted surgeryrobotic total knee arthroplastytotal knee arthroplasty

Identifiers

PMID38929810
PMCPMC11204817

What OpenQuestion holds

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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.