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Record W4376121750 · doi:10.1016/j.spinee.2023.05.001

Determining minimal clinically important difference estimates following surgery for degenerative conditions of the lumbar spine: analysis of the Canadian Spine Outcomes and Research Network (CSORN) registry

2023· article· en· W4376121750 on OpenAlexafffundabout
J. Denise Power, Anthony V. Perruccio, Mayilée Cañizares, Greg McIntosh, Edward Abraham, Najmedden Attabib, Christopher S. Bailey, Raphaële Charest-Morin, Nicholas Dea, Joel Finkelstein, Charles G. Fisher, Andrew Glennie, Hamilton Hall, Michael G. Johnson, Adrienne Kelly, Stephen Kingwell, Neil Manson, Andrew Nataraj, Jérôme Paquet, Supriya Singh, Alex Soroceanu, Kenneth Thomas, Michael H. Weber, Y. Raja Rampersaud

Bibliographic record

VenueThe Spine Journal · 2023
Typearticle
Languageen
FieldMedicine
TopicSpine and Intervertebral Disc Pathology
Canadian institutionsUniversity of CalgaryUniversité LavalUniversity of AlbertaMcGill University Health CentreUniversity of OttawaNOSM UniversityHôpital de l'Enfant-JésusUniversity of ManitobaUniversity Health NetworkHealth Sciences CentreSault Area HospitalSunnybrook Health Science CentreHorizon Health NetworkLondon Health Sciences CentreCanada East Spine CentreUniversity of TorontoWestern UniversityUniversity of British ColumbiaDalhousie University
FundersArthritis SocietyStrykerRick Hansen InstituteNuVasiveOrthopaedic Research and Education Foundation
KeywordsMedicineMinimal clinically important differenceOswestry Disability IndexSpondylolisthesisPhysical therapyDegenerative disc diseasePopulationLow back painLumbar spineLumbarReceiver operating characteristicSurgeryBack painRandomized controlled trialInternal medicinePathology

Abstract

fetched live from OpenAlex

BACKGROUND CONTEXT: There is significant variability in minimal clinically important difference (MCID) criteria for lumbar spine surgery that suggests population and primary pathology specific thresholds may be required to help determine surgical success when using patient reported outcome measures (PROMs). PURPOSE: The purpose of this study was to estimate MCID thresholds for 3 commonly used PROMs after surgical intervention for each of 4 common lumbar spine pathologies. STUDY DESIGN/SETTING: Observational longitudinal study of patients from the Canadian Spine Outcomes and Research Network (CSORN) national registry. PATIENT SAMPLE: Patients undergoing surgery from 2015 to 2018 for lumbar spinal stenosis (LSS; n = 856), degenerative spondylolisthesis (DS; n = 591), disc herniation (DH; n = 520) or degenerative disc disease (DDD n = 185) were included. OUTCOME MEASURES: PROMs were collected presurgery and 1-year postsurgery: the Oswestry Disability Index (ODI), and back and leg Numeric Pain Rating Scales (NPRS). At 1-year, patients reported whether they were 'Much better'/'Better'/'Same'/'Worse'/'Much worse' compared to before their surgery. Responses to this item were used as the anchor in analyses to determine surgical MCIDs for benefit ('Much better'/'Better') and substantial benefit ('Much better'). METHODS: MCIDs for absolute and percentage change for each of the 3 PROMs were estimated using a receiving operating curve (ROC) approach, with maximization of Youden's index as primary criterion. Area under the curve (AUC) estimates, sensitivity, specificity and correct classification rates were determined. All analyses were conducted separately by pathology group. RESULTS: MCIDs for ODI change ranged from -10.0 (DDD) to -16.9 (DH) for benefit, and -13.8 (LSS) to -22.0 (DS,DH) for substantial benefit. MCID for back and leg NPRS change were -2 to -3 for each group for benefit and -4.0 for substantial benefit for all groups on back NPRS. MCID estimates for percentage change varied by PROM and pathology group, ranging from -11.1% (ODI for DDD) to -50.0% (leg NPRS for DH) for benefit and from -40.0% (ODI for DDD) to -66.6% (leg NPRS for DH) for substantial benefit. Correct classification rates for all MCID thresholds ranged from 71% to 89% and were relatively lower for absolute vs percent change for those with high or low presurgical scores. CONCLUSIONS: Our findings suggest that the use of generic MCID thresholds across pathologies in lumbar spine surgery is not recommended. For patients with relatively low or high presurgery PROM scores, MCIDs based on percentage change, rather than absolute change, appear generally preferable. These findings have applicability in clinical and research settings, and are important for future surgical prognostic work.

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)0.018
metaresearch head score (Gemma)0.049
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 score0.790
Threshold uncertainty score0.423

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0180.049
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.007
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.001
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 head0.103
GPT teacher head0.420
Teacher spread0.316 · 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

Citations40
Published2023
Admission routes3
Has abstractyes

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