Evidence map›Paper›PMID 37164480›Full record

ArticleInternational journal of spine surgery2023

Future Data Points to Implement in Adult Spinal Deformity Assessment for Artificial Intelligence Modeling Prediction: The Importance of the Biological Dimension.

Sleiman Haddad, Javier Pizones, Riccardo Raganato, Michael M Safaee, Justin K Scheer, Ferran Pellisé, Christopher P Ames

Abstract read
In one paragraph

Article in International journal of spine surgery, 2023. 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. Adult spinal deformity correction surgery using age-adjusted alignment thresholds: clinical outcomes and mechanical complication rates. A systematic review of the literature.European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society · 2024
    Pooled it
  2. The weight of the most common biological variables in the prediction of adverse events after adult spinal deformity surgery.European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society · 2026
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  9. Emerging Technologies within Spine Surgery.Life (Basel, Switzerland) · 2023
    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.

Sleiman HaddadSpine Surgery Unit, Hospital Universitario Vall d'Hebron, Barcelona, Spain.
Javier PizonesSpine Unit, Department of Orthopedic Surgery, Hospital Universitario La Paz, Madrid, Spain javierpizones@gmail.com.
Riccardo RaganatoSpine Unit, Department of Orthopedic Surgery, Hospital Universitario La Paz, Madrid, Spain.
Michael M SafaeeDepartment of Neurological Surgery, University of California, San Francisco, CA, USA.
Justin K ScheerDepartment of Neurological Surgery, University of California, San Francisco, CA, USA.
Ferran PelliséSpine Surgery Unit, Hospital Universitario Vall d'Hebron, Barcelona, Spain.
Christopher P AmesDepartment of Neurological Surgery, University of California, San Francisco, CA, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Adult spinal deformity (ASD) surgery is still associated with high surgical risks. Machine learning algorithms applied to multicenter databases have been created to predict outcomes and complications, optimize patient selection, and improve overall results. However, the multiple data points currently used to create these models allow for 70% of accuracy in prediction. We need to find new variables that can capture the spectrum of probability that is escaping from our control. These proposed variables are based on patients' biological dimensions, such as frailty, sarcopenia, muscle and bone (tissue) sampling, serological assessment of cellular senescence, and circulating biomarkers that can measure epigenetics, inflammaging, and -omics. Many of these variables are proven to be modifiable and could be improved with proper nutrition, toxin avoidance, endurance exercise, and even surgery. The purpose of this manuscript is to describe the different future data points that can be implemented in ASD assessment to improve modeling prediction, allow monitoring their response to prerehabilitation programs, and improve patient counseling.

Indexed as

artificial intelligencebiomarkersfrailtymetabolomicsosteoporosissarcopeniasenescencespinal deformitiestissue sample

Identifiers

PMID37164480
PMCPMC10318909

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.