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Record W2494624591 · doi:10.1007/s11999-016-5021-y

Can Surgeons Adequately Capture Adverse Events Using the Spinal Adverse Events Severity System (SAVES) and OrthoSAVES?

2016· article· en· W2494624591 on OpenAlexafffund
Brian Po‐Jung Chen, Darren M. Roffey, Stéphane Poitras, Geoffrey F. Dervin, Peter Lapner, Philippe Phan, Eugene K. Wai, Stephen Kingwell, Paul E. Beaulé

Bibliographic record

VenueClinical Orthopaedics and Related Research · 2016
Typearticle
Languageen
FieldHealth Professions
TopicPatient Safety and Medication Errors
Canadian institutionsOttawa HospitalUniversity of Ottawa
FundersOttawa Hospital Research Institute
KeywordsMedicineAdverse effectSpinal manipulationMEDLINEIntensive care medicineSurgeryInternal medicineLow back painAlternative medicinePathology

Abstract

fetched live from OpenAlex

BACKGROUND: Physicians have consistently shown poor adverse-event reporting practices in the literature and yet they have the clinical acumen to properly stratify and appraise these events. The Spine Adverse Events Severity System (SAVES) and Orthopaedic Surgical Adverse Events Severity System (OrthoSAVES) are standardized assessment tools designed to record adverse events in orthopaedic patients. These tools provide a list of prespecified adverse events for users to choose from-an aid that may improve adverse-event reporting by physicians. QUESTIONS/PURPOSES: The primary objective was to compare surgeons' adverse-event reporting with reporting by independent clinical reviewers using SAVES Version 2 (SAVES V2) and OrthoSAVES in elective orthopaedic procedures. METHOD: This was a 10-week prospective study where SAVES V2 and OrthoSAVES were used by six orthopaedic surgeons and two independent, non-MD clinical reviewers to record adverse events after all elective procedures to the point of patient discharge. Neither surgeons nor reviewers received specific training on adverse-event reporting. Surgeons were aware of the ongoing study, and reported adverse events based on their clinical interactions with the patients. Reviewers recorded adverse events by reviewing clinical notes by surgeons and other healthcare professionals (such as nurses and physiotherapists). Adverse events were graded using the severity-grading system included in SAVES V2 and OrthoSAVES. At discharge, adverse events recorded by surgeons and reviewers were recorded in our database. RESULTS: Adverse-event data for 164 patients were collected (48 patients who had spine surgery, 51 who had hip surgery, 34 who had knee surgery, and 31 who had shoulder surgery). Overall, 99 adverse events were captured by the reviewers, compared with 14 captured by the surgeons (p < 0.001). Surgeons adequately captured major adverse events, but failed to record minor events that were captured by the reviewers. A total of 93 of 99 (94%) adverse events reported by reviewers required only simple or minor treatment and had no long-term adverse effect. Three patients experienced adverse events that resulted in use of invasive or complex treatment that had a temporary adverse effect on outcome. CONCLUSION: Using SAVES V2 and OrthoSAVES, independent reviewers reported more minor adverse events compared with surgeons. The value of third-party reviewers requires further investigation in a detailed cost-benefit analysis. LEVEL OF EVIDENCE: Level II, therapeutic study.

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)0.011
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.027
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0110.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.001
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0010.003
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 head0.214
GPT teacher head0.501
Teacher spread0.287 · 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.

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

Citations34
Published2016
Admission routes2
Has abstractyes

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