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Record W3096831210 · doi:10.1097/brs.0000000000003766

Development and Validation of a Multidomain Surgical Complication Classification System for Adult Spinal Deformity

2020· article· en· W3096831210 on OpenAlexaff
Eric O. Klineberg, Joseph B. Wick, Renaud Lafage, Virginie Lafage, Ferrán Pellisé, Sleiman Haddad, Çağlar Yılgör, Susana Núñez-Pereira, Munish C. Gupta, Justin S. Smith, Christopher I. Shaffrey, Frank J. Schwab, Christopher P. Ames, Shay Bess, Stephen J. Lewis, Lawrence G. Lenke, Sigurd Berven

Bibliographic record

VenueSpine · 2020
Typearticle
Languageen
FieldMedicine
TopicScoliosis diagnosis and treatment
Canadian institutionsToronto Western HospitalUniversity of Toronto
Fundersnot available
KeywordsMedicineDeformityComplicationSpinal deformitySurgerySurgical complicationRepeatabilityRachis

Abstract

fetched live from OpenAlex

STUDY DESIGN: Prospective analysis of example cases. OBJECTIVE: The aim of this study was to analyze the accuracy and repeatability of a new comprehensive classification system for capturing complications data in adult spinal deformity. SUMMARY OF BACKGROUND DATA: Complications are common in adult spinal deformity surgery. However, no consensus exists on the definition or classification of complications in adult spinal deformity surgery. The lack of consensus significantly limits understanding of complications' effects on outcomes in surgery for adult spinal deformity. METHODS: Using a Delphi method, members of the International Spine Study Group, AO Spine, and the European Spine Study Group collaborated to develop an adult spinal deformity classification system. The multidomain classification system accounts for medical complications (cancer, cardiopulmonary, central nervous system, gastrointestinal, infectious, musculoskeletal, renal) and surgical complications (implant complications, radiographic complications, neurologic events, intraoperative events, and wound complications). Seventeen individuals ("event readers"), including spine surgeons, trainees, and research coordinators, used the new classification system two separate times to analyze complications in ten example cases. The accuracy and repeatability of the classification system were subsequently calculated based on the providers' responses for the example cases. RESULTS: The 10 example cases included 22 complications. Nearly 95% of complications were captured by >95% of the event readers. The system demonstrated good repeatability of 86.9% between the first and second set of responses provided by event readers. CONCLUSION: The ISSG-AO Multi-Domain Spinal Deformity Complication Classification System for Adult Spinal Deformity demonstrated good accuracy and repeatability among both surgeons and research coordinators in capturing complications in adult spinal deformity surgery. The ISSG-AO system may be applied to help better understand the impact of complications on outcomes and costs in adult spinal deformity surgery.Level of Evidence: 5.

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)0.060
metaresearch head score (Gemma)0.089
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: Methods · Consensus signal: none
Teacher disagreement score0.060
Threshold uncertainty score0.315

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0600.089
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.002
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 head0.078
GPT teacher head0.326
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
GenreMethods

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

Citations33
Published2020
Admission routes1
Has abstractyes

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