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Record W2911051318 · doi:10.1016/j.spinee.2019.01.003

Effect of spinal decompression on back pain in lumbar spinal stenosis: a Canadian Spine Outcomes Research Network (CSORN) study

2019· article· en· W2911051318 on OpenAlexafffundabout
Shreya Srinivas, Jérôme Paquet, Christopher S. Bailey, Andrew Nataraj, Alexandra Stratton, Michael G. Johnson, Paul Salo, Sean Christie, Charles G. Fisher, Hamilton Hall, Neil Manson, Y. Raja Rampersaud, Kenneth Thomas, Greg McIntosh, Nicloas Dea

Bibliographic record

VenueThe Spine Journal · 2019
Typearticle
Languageen
FieldMedicine
TopicSpine and Intervertebral Disc Pathology
Canadian institutionsCanadian Respiratory Research NetworkToronto Western HospitalCanada East Spine CentreUniversity of TorontoQueen Elizabeth II Health Sciences CentreUniversity Health NetworkUniversity of Alberta HospitalOttawa HospitalHealth Sciences CentreFoothills Medical CentreLondon Health Sciences CentreVictoria HospitalHôpital de l'Enfant-JésusUniversity of CalgaryVancouver Spine Surgery Institute
FundersDePuy Synthes SpineRick Hansen InstituteCanadian Institutes of Health ResearchMedtronic
KeywordsMedicineLumbar spinal stenosisNeurogenic claudicationDecompressionSurgeryLow back painBack painSpinal stenosisClaudicationLumbarSpinal canal stenosisPhysical therapySpinal canalVascular diseaseSpinal cord

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.002
metaresearch head score (Gemma)0.006
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 score0.051
Threshold uncertainty score0.117

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.003
Bibliometrics0.0010.003
Science and technology studies0.0030.002
Scholarly communication0.0020.001
Open science0.0020.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.042
GPT teacher head0.403
Teacher spread0.361 · 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

Citations37
Published2019
Admission routes3
Has abstractno

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