Evidence map›Paper›PMID 38638115›Full record

ArticleJournal of orthopaedics2024

Artificial neural network analysis of factors affecting functional independence recovery in patients with lumbar stenosis after neurosurgery treatment: An observational cohort study.

Alex Martino Cinnera, Giovanni Morone, Marco Iosa, Stefano Bonomi, Rocco Salvatore Calabrò, Paolo Tonin, Antonio Cerasa, Alessandro Ricci, Irene Ciancarelli

Open access · greenAbstract read
In one paragraph

Article in Journal of orthopaedics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed, 1 pooled it
1.7field-weighted citation impact, top 17% of its field
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

2 citing papers in PubMed, 1 synthesis or guideline pooled it, 4 citations in OpenAlex.

  1. Pooled it
  2. Article
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

9 authors at 6 institutions in 1 country.

Alex Martino CinneraIRCCS Santa Lucia Foundation Hospital, Rome, Italy.
Giovanni MoroneDepartment of Life, Health and Environmental Sciences, University of L'Aquila, L'Aquila, Italy.
Marco IosaIRCCS Santa Lucia Foundation Hospital, Rome, Italy.
Stefano BonomiCattolica del Sacro Cuore University, Rome, Italy.
Rocco Salvatore CalabròIRCCS Centro Neurolesi "Bonino Pulejo", Messina, Italy.
Paolo ToninSant'Anna Institute, Crotone, Italy.
Antonio CerasaSant'Anna Institute, Crotone, Italy.
Alessandro RicciDepartment of Neurosurgery, San Salvatore Hospital, ASL Avezzano-Sulmona-L'Aquila, L'Aquila, Italy.
Irene CiancarelliDepartment of Life, Health and Environmental Sciences, University of L'Aquila, L'Aquila, Italy.
Fondazione Santa Lucia · ITIstituto S.Anna Crotone · ITUniversity of L'Aquila · ITIstituti di Ricovero e Cura a Carattere Scientifico · ITSan Salvatore Hospital · ITUniversità Cattolica del Sacro Cuore · IT

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background and aim: Lumbar spinal stenosis (LSS) is a leading cause of low back pain and lower limbs pain often associated with functional impairment which entails the loss or the impairment of independence in older adults. Conservative treatment is effective in a small percentage of patients, while a significant percentage undergo surgery, even if often without a complete resolution of clinical symptoms and motor deficits. The aim of the study is to identify clinical and demographic prognostic factors characterising the patients who would benefit most from surgical treatment in relation to the functional independence recovery using an innovative approach based on an artificial neural network. Methods: Adult patients with LSS and indication of neurosurgical treatment were enrolled in the study. Clinical evaluation was performed in the preoperative-phase (into the 48 h before surgery) and after two months. Clinical battery investigated the motor, functional, cognitive, behavioural, and pain status. Demographics and clinical characteristics were analysed via Artificial Neural Network (ANN) using 24 input variables, 2 hidden layers and a single final output layer to predict the outcome. ANN results were compared with those of a multiple linear regression. Results: 108 patients were included in the study and 90 of them [66.5 ± 12.8 years; 27.8 % F] were submitted to surgery treatment and completed longitudinal evaluation. Statistically significant improvement was recorded in all clinical scales comparing pre- and post-surgery. The ANN results showed a prediction ability up to 81 %. Disability, functional limitations, and pain concerning clinical assessment and stature, onset and age about demographic characteristics are the main variables impacting on surgical outcome. Conclusions: ANN can support clinical decision making, using clinical and demographic characteristics of patients with LSS identifying the characteristics of those who might benefit more from the surgical treatment in terms of global functional recovery.

Indexed as

Artificial intelligenceDeep learningLumbar spinal stenosisMachine learning

Identifiers

PMID38638115
PMCPMC11021912
OpenAlexW4394685187

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.