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SURGERY FOR ADULT SPINAL SCOLIOSIS: DO THE BENEFITS OUTWEIGH THE RISKS?

2019· article· en· W2970562415 on OpenAlexaff
Jeferson Dedéa, Alisson Roberto Teles, Asdrúbal Falavigna

Bibliographic record

VenueColuna/Columna · 2019
Typearticle
Languageen
FieldMedicine
TopicScoliosis diagnosis and treatment
Canadian institutionsMcGill University
Fundersnot available
KeywordsMedicinePerioperativeQuality of life (healthcare)ScoliosisSurgeryConservative treatmentSpinal deformityInvasive surgerySpinal surgeryDeformity

Abstract

fetched live from OpenAlex

ABSTRACT Around 6% of the elderly population over 65 years of age are affected by adult spinal deformity (ASD). The increasing prevalence of ASD with aging has prompted discussion regarding the use of various techniques for its treatment, such as surgery and conservative treatment. The objective of this study is to investigate whether surgical treatment demonstrates significant benefits as compared to conservative treatment. A literature review was conducted, focusing on the most relevant papers on the topic published in the last five years. Surgical treatment, which costs an average of US$ 99,114 per procedure, enables almost instant improvement of the pain and disability of ASD. The rate of perioperative complications in ASD is approximately 7.5%, and the average improvement in back pain is 6.2 times higher in the surgical approach than in conservative treatment. In addition, the use of modern operative techniques, such as minimally invasive surgery (MIS), reduces the complications and greatly improves patients’ quality of life, compared to open surgery. Therefore, the expected benefits of surgical treatment meets the patient’s expectations by eliminating the pain caused by ASD. Although surgical treatment has a higher cost and a greater risk of complications, the use of minimally invasive techniques give the ASD patient a better quality of life. Level of evidence III; Non-Systematic Review.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.113
Threshold uncertainty score0.739

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.060
GPT teacher head0.319
Teacher spread0.259 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2019
Admission routes1
Has abstractyes

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