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Record W4416150185 · doi:10.1097/brs.0000000000005566

Impact of Complications on DRG Assignment for Adult Spinal Deformity Surgery Using the ISSG-AO Classification System

2025· article· en· W4416150185 on OpenAlexaffabout
Pratibha Nayak, Richard A. Hostin, Eric O. Klineberg, Renaud Lafage, Brendan T. Oreilly, Breton Line, Peter G. Passias, Shay Bess, Khaled M. Kebaish, Lawrence G. Lenke, Christopher I. Shaffrey, Alan H. Daniels, Bassel G. Diebo, Christopher P. Ames, Doug Burton, Stephen J. Lewis, Robert K. Eastlack, Gregory M. Mundis, Pierce D. Nunley, Robert A. Hart, Jeffrey P. Mullin, D. Kojo Hamilton, Virginie Lafage, Munish C Gupta, Michael P. Kelly, Themistocles S. Protopsaltis, Han Jo Kim, Frank J. Schwab, Justin S. Smith, Jeffery L. Gum

Bibliographic record

VenueSpine · 2025
Typearticle
Languageen
FieldMedicine
TopicScoliosis diagnosis and treatment
Canadian institutionsToronto Western HospitalUniversity Health Network
Fundersnot available
KeywordsSpinal deformityComplicationSpinal surgeryMEDLINERachisDeformity

Abstract

fetched live from OpenAlex

STUDY DESIGN: Retrospective cohort. OBJECTIVE: The ISSG-AO Spinal Deformity Complication Classification System (SDCCS) predicts Diagnosis-Related Group (DRG) coding and cost. BACKGROUND: Inconsistent definitions of complications contribute to variation in reported surgical complication rates. Incorrect complication reporting can lead to over or under DRG reimbursement. The ISSG-AO SDCCS provides improved complication reporting reproducibility and may help predict complication costs. MATERIALS AND METHODS: ASD patients were grouped into DRG without complication or comorbidity (CC) or Major CC (MCC) (DRGs 455 and 458), with CC (DRGs 454 and 457), and with MCC (DRGs 453 and 456). Complications were graded by intervention severity per ISSG-AO system: grade 0 (none), 1 (mild- e.g. , med change), 2 (moderate- e.g. , ICU), 3 (severe- e.g. , reoperation). Costs were based on the Medicare inpatient prospective payment system (IPSS, Medicare Allowable rate). A multinomial logistic model identified key predictors of DRG assignment by complication grades. RESULTS: Of the 675 patients, 14% were in DRGs without CC/MCC, 71% in DRGs with CC, and 15% were in DRGs with MCC. Patients with complications requiring intervention mostly fell into the higher DRG categories (97%). Patients who received an intervention are 6.75 (2.01-22.75, P <0.0021) times more likely to be classified under DRG with CC and 15.72 (95% CI: 4.23-58.45, P <0.0001) times more likely to be classified with DRG with MCC compared with those who did not receive an intervention. Each unit increase in Edmonton Frailty Score raises the odds of being in DRG with MCC by 1.24 (95% CI: 1.04-1.48, P =0.017). Similar trends were seen for OR time and LOS. Reimbursement showed incremental increase from $49.5K to $56K to $70K across DRG categories. CONCLUSIONS: Patients with elevated ISSG-AO scores are more likely to be categorized into higher DRGs, experience extended lengths of stay and generate greater health care expenditures. The ISSG-AO SDCCS predicts DRG, thereby helping standardize complication reporting.

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)0.009
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 score0.007
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.002
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
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 head0.106
GPT teacher head0.411
Teacher spread0.305 · 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".

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Citations0
Published2025
Admission routes2
Has abstractyes

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