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Record W2999359313 · doi:10.1177/2192568219839699

Quality and Safety Improvement in Spine Surgery

2020· article· en· W2999359313 on OpenAlexaff
Fan Jiang, Jamie Wilson, Jetan H. Badhiwala, Carlo Santaguida, Michael H. Weber, Jefferson R. Wilson, Michael G. Fehlings

Bibliographic record

VenueGlobal Spine Journal · 2020
Typearticle
Languageen
FieldMedicine
TopicSpine and Intervertebral Disc Pathology
Canadian institutionsMcGill University Health CentreSt. Michael's HospitalToronto Western HospitalUniversity of TorontoUniversity Health Network
Fundersnot available
KeywordsMedicineQuality managementPatient safetyQuality (philosophy)MEDLINEHealth careNarrative reviewAdverse effectIntensive care medicineOperations management

Abstract

fetched live from OpenAlex

STUDY DESIGN: Review article. OBJECTIVES: A narrative review of the literature on the current advances and limitations in quality and safety improvement initiatives in spine surgery. METHODS: A comprehensive literature search was performed using Ovid MEDLINE focusing on 3 preidentified concepts: (1) quality and safety improvement, (2) reporting of outcomes and adverse events, and (3) prediction model and practice guidelines. The search was conducted under appropriate subject headings and using relevant text words. Articles were screened, and manuscripts relevant to this discussion were included in the narrative review. RESULTS: Quality and safety improvement remains a major research focus attracting investigators from the global spine community. Multiple databases and registries have been developed for the purpose of generating data and monitoring the progress of quality and safety improvement initiatives. The development of various prediction models and clinical practice guidelines has helped shape the care of spine patients in the modern era. With the reported success of exemplary programs initiated by the Northwestern and Seattle Spine Team, other quality and safety improvement initiatives are anticipated to follow. However, despite these advancements, the reporting metrics for outcomes and adverse events remain heterogeneous in the literature. CONCLUSION: Constant surveillance and continuous improvement of the quality and safety of spine treatments is imperative in modern health care. Although great advancement has been made, issues with reporting outcomes and adverse events persist, and improvement in this regard is certainly needed.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.477
Threshold uncertainty score0.455

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.051
GPT teacher head0.345
Teacher spread0.294 · 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 teacher head, 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

Citations16
Published2020
Admission routes1
Has abstractyes

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