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Record W3204952590 · doi:10.3171/2021.5.spine21192

Predictors of long-term clinical outcomes in adult patients after lumbar total disc replacement: development and validation of a prediction model

2021· article· en· W3204952590 on OpenAlexaff
Domagoj Coric, Jack E. Zigler, Peter B. Derman, Ernest Braxton, Aaron Situ, Leena Patel

Bibliographic record

VenueJournal of Neurosurgery Spine · 2021
Typearticle
Languageen
FieldMedicine
TopicSpine and Intervertebral Disc Pathology
Canadian institutionsEVERSANA (Canada)
Fundersnot available
KeywordsMedicineOswestry Disability IndexVisual analogue scalePhysical therapyLogistic regressionRandomized controlled trialReceiver operating characteristicRandomizationBack painLow back painLumbarMinimal clinically important differenceClinical trialSurgeryInternal medicine

Abstract

fetched live from OpenAlex

OBJECTIVE: Long-term outcomes of single-level lumbar arthroplasty are understood to be very good, with the most recent Investigational Device Exemption (IDE) trial showing a < 5% reoperation rate at the close of the 7-year study. This post hoc analysis was conducted to determine whether specific patients from the activL IDE data set had better outcomes than the mean good outcome of the IDE trial, as well as to identify contributing factors that could be optimized in real-world use. METHODS: Univariable and multivariable logistic regression models were developed using the randomized patient set (n = 283) from the activL trial and used to identify predictive factors and to derive risk equations. The models were internally validated using the randomized patient set and externally validated using the nonrandomized patient set (n = 52) from the activL trial. Predictive power was assessed using area under the receiver operating characteristic curve analysis. RESULTS: Two factors were significantly associated with achievement of better than the mean outcomes at 7 years. Randomization to receive the activL device was positively associated with better than the mean visual analog scale (VAS)-back pain and Oswestry Disability Index (ODI) scores, whereas preoperative narcotics use was negatively associated with better than the mean ODI score. Preoperative narcotics use was also negatively associated with return to unrestricted full-time work. Other preoperative factors associated with positive outcomes included unrestricted full-time work, working manual labor after index back injury, and decreasing disc height. Older age, greater VAS-leg pain score, greater ODI score, female sex, and working manual labor before back injury were identified as preoperative factors associated with negative outcomes. Preoperative BMI, VAS-back pain score, back pain duration ≥ 1 year, SF-36 physical component summary score, and recreational activity had no effect on outcomes. CONCLUSIONS: Lumbar total disc replacement for symptomatic single-level lumbar degenerative disc disease is a well-established option for improving long-term patient outcomes. Discontinuing narcotics use may further improve patient outcomes, as this analysis identified associations between no preoperative narcotics use and better ODI score relative to the mean score of the activL trial at 7 years and increased likelihood of return to work within 7 years. Other preoperative factors that may further improve outcomes included unrestricted full-time work, working manual labor despite back injury, sedentary work status before back injury, and randomization to receive the activL device. Tailoring patient care before total disc replacement may further improve patient outcomes.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.016
metaresearch head score (Gemma)0.034
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.016
Threshold uncertainty score0.083

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0160.034
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0010.000

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 head0.027
GPT teacher head0.307
Teacher spread0.280 · 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 source (direct Gemma or distilled Codex), 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

Citations8
Published2021
Admission routes1
Has abstractyes

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