Evidence map›Paper›PMID 36128054›Full record

ArticleNMC case report journal2022

Preoperative Planning Using Three-dimensional Printing for Full-endoscopic Spine Surgery: A Technical Note.

Ryo Okada, Toshinori Sakai, Toshihiko Nishisho, Akihiro Nitta, Shigeyuki Takahara, Koichi Oba, Koichi Sairyo

Abstract readCase Reports
In one paragraph

Article in NMC case report journal, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

  1. Review
  2. Review
  3. Article
  4. Article
  5. Review
  6. Recent advances of 3D-printing in spine surgery.Surgical neurology international · 2024
    Review
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

7 authors.

Ryo OkadaTokushima Prefectural Miyoshi Hospital, Miyoshi, Tokushima, Japan.
Toshinori SakaiDepartment of Orthopedics, Tokushima University Graduate School, Tokushima, Tokushima, Japan.
Toshihiko NishishoTokushima Prefectural Miyoshi Hospital, Miyoshi, Tokushima, Japan.
Akihiro NittaTokushima Prefectural Miyoshi Hospital, Miyoshi, Tokushima, Japan.
Shigeyuki TakaharaTokushima Prefectural Miyoshi Hospital, Miyoshi, Tokushima, Japan.
Koichi ObaTokushima Prefectural Miyoshi Hospital, Miyoshi, Tokushima, Japan.
Koichi SairyoDepartment of Orthopedics, Tokushima University Graduate School, Tokushima, Tokushima, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Transforaminal full-endoscopic spine surgery (TF-FESS) is a novel minimally invasive spine surgery that requires only an 8-mm skin incision and causes minimal damage to the paravertebral muscles. To perform TF-FESS safely and efficiently, preoperative planning is quite important as the intervention requires anatomical understanding and high technical skills. Recently, three-dimensional (3D) printing has become a useful tool in various surgeries, and several studies have addressed its efficacy; however, there are no reports on the application of 3D printing to FESS. In this study, we present two cases of severe lumbar deformities for which preoperative 3D printing was useful. The 3D printing enabled the surgeons to visualize and plan the drilling of the superior articular process for a successful foraminoplasty at a low cost. The manufacturing equipment cost about USD 900 and is able to produce an actual-size model at a cost of less than USD 10 per patient. In conclusion, preoperative planning using 3D printing should be adopted to safely perform FESS.

Indexed as

3D printingfull-endoscopic spine surgery (FESS)lumbar spine

Identifiers

PMID36128054
PMCPMC9458159

What OpenQuestion holds

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LicenceCC BY-NC-ND
Read underepoch 390

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.