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Record W4310335490 · doi:10.2106/jbjs.oa.22.00052

Evaluating Instability in Degenerative Lumbar Spondylolisthesis

2022· article· en· W4310335490 on OpenAlexaff
Mark A. MacLean, Christopher S. Bailey, Charles G. Fisher, Y. Raja Rampersaud, Ryan Greene, Edward Abraham, Nicholas Dea, Hamilton Hall, Neil Manson, Andrew Glennie

Bibliographic record

VenueJBJS Open Access · 2022
Typearticle
Languageen
FieldMedicine
TopicSpine and Intervertebral Disc Pathology
Canadian institutionsUniversity of TorontoUniversity of British ColumbiaWestern UniversitySaint John Regional HospitalDalhousie University
Fundersnot available
KeywordsMedicineSpondylolisthesisInstabilityLumbarLow back painSurgery

Abstract

fetched live from OpenAlex

The subjective degenerative spondylolisthesis instability classification (S-DSIC) system attempts to define preoperative instability associated with degenerative lumbar spondylolisthesis (DLS). The system guides surgical decision-making based on numerous indicators of instability that surgeons subjectively assess and incorporate. A more objective classification is warranted in order to decrease variation among surgeons. In this study, our objectives included (1) proposing an objective version of the DSIC system (O-DSIC) based on the best available clinical and biomechanical data and (2) comparing subjective surgeon perceptions (S-DSIC) with an objective measure (O-DSIC) of instability related to DLS. Methods: In this multicenter cohort study, we prospectively enrolled 408 consecutive adult patients who received surgery for symptomatic DLS. Surgeons prospectively categorized preoperative instability using the existing S-DSIC system. Subsequently, an O-DSIC system was created. Variables selected for inclusion were assigned point values based on previously determined evidence quality. DSIC types were derived by point summation: 0 to 2 points was considered stable, Type I); 3 points, potentially unstable, Type II; and 4 to 5 points, unstable, Type III. Surgeons' subjective perceptions of instability (S-DSIC) were retrospectively compared with O-DSIC types. Results: The O-DSIC system includes 5 variables: presence of facet effusion, disc height preservation (≥6.5 mm), translation (≥4 mm), a kyphotic or neutral disc angle in flexion, and low back pain (≥5 of 10 intensity). Type I (n = 176, 57.0%) and Type II (n = 164, 53.0%) were the most common DSIC types according to the O-DSIC and S-DSIC systems, respectively. Surgeons categorized higher degrees of instability with the S-DSIC than the O-DSIC system in 130 patients (42%) (p < 0.001). The assignment of DSIC types was not influenced by demographic variables with either system. Conclusions: The O-DSIC system facilitates objective assessment of preoperative instability related to DLS. Surgeons assigned higher degrees of instability with the S-DSIC than the O-DSIC system in 42% of cases. Level of Evidence: Diagnostic Level II. See Instructions for Authors for a complete description of levels of evidence.

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)0.003
metaresearch head score (Gemma)0.010
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 score0.003
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0030.002
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
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 head0.297
GPT teacher head0.546
Teacher spread0.248 · 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

Citations9
Published2022
Admission routes1
Has abstractyes

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