MétaCan
Menu
Back to cohort
Record W2399163988 · doi:10.1227/neu.0000000000000940

Impact of Movement Disorders on Management of Spinal Deformity in the Elderly

2015· review· en· W2399163988 on OpenAlexaff
Yoon Ha, Jae Keun Oh, Justin S. Smith, Tamir Ailon, Michael G. Fehlings, Christopher I. Shaffrey, Christopher P. Ames

Bibliographic record

VenueNeurosurgery · 2015
Typereview
Languageen
FieldMedicine
TopicParkinson's Disease and Spinal Disorders
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMedicineScoliosisSpinal deformitySpinal fusionSpinal surgeryDeformitySagittal planeSpinal muscular atrophySurgeryMovement disordersDiseasePhysical medicine and rehabilitationRadiology

Abstract

fetched live from OpenAlex

Spinal deformities are frequent and disabling complications of movement disorders such as Parkinson disease and multiple system atrophy. The most distinct spinal deformities include camptocormia, antecollis, Pisa syndrome, and scoliosis. Spinal surgery has become lower risk and more efficacious for complex spinal deformities, and thus more appealing to patients, particularly those for whom conservative treatment is inappropriate or ineffective. Recent innovations and advances in spinal surgery have revolutionized the management of spinal deformities in elderly patients. However, spinal deformity surgeries in patients with Parkinson disease remain challenging. High rates of mechanical complications can necessitate revision surgery. The success of spinal surgery in patients with Parkinson disease depends on an interdisciplinary approach, including both surgeons and movement disorder specialists, to select appropriate surgical patients and manage postoperative movement in order to decrease mechanical failures. Achieving appropriate correction of sagittal alignment with strong biomechanical instrumentation and bone fusion is the key determinant of satisfactory results.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.967
Threshold uncertainty score0.891

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.064
GPT teacher head0.375
Teacher spread0.311 · 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 designOther design
Domainnot available
GenreReview

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

Citations34
Published2015
Admission routes1
Has abstractyes

Explore more

Same venueNeurosurgerySame topicParkinson's Disease and Spinal DisordersFrench-language works237,207