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Record W3014726297 · doi:10.1177/2325967120s00120

Surgical Outcomes In The Frequency, Etiology, Direction, Severity (feds) Classification System For Shoulder Instability

2020· article· en· W3014726297 on OpenAlexaboutno aff
Justin A. Magnuson, Brian R. Wolf, Kevin Cronin, Cale A. Jacobs, Shannon F. Ortiz, John E. Kuhn, Moon Shoulder Group, Carolyn M. Hettrich

Bibliographic record

VenueOrthopaedic Journal of Sports Medicine · 2020
Typearticle
Languageen
FieldMedicine
TopicShoulder Injury and Treatment
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineSubluxationEtiologyElbowSurgeryCohortAnterior shoulderEpidemiologyInternal medicine

Abstract

fetched live from OpenAlex

Objectives: The Frequency, Etiology, Direction, Severity (FEDS) system is a reliable and reproducible classification of glenohumeral instability. Frequency is defined as Solitary (1), Occasional (2-5), or Frequent (>5) episodes per year; etiology as Traumatic or Atraumatic; direction as Anterior, Posterior, or Inferior; and severity as a Subluxation or Dislocation. 36 total combinations are possible, named by the first letter of each variable in order. The purpose of this descriptive study was to investigate epidemiology, surgical outcomes, and failure using FEDS in patients undergoing surgery in a large multicenter cohort of prospectively enrolled patients. Methods: 1204 patients undergoing surgery were assigned to FEDS categories. Two-year follow-up at time of analysis was available for 629 patients (85.7% of those eligible based on date of surgery). Those categories consisting of at least 5% of patients were further analyzed by patient reported outcomes (PROs) and failure rates for a total of 466 patients. PROs included American Shoulder and Elbow Surgeons score (ASES), Western Ontario Shoulder Instability index (WOSI), and Single Assessment Numeric Evaluation (SANE). Failure benchmarks included rates of recurrent subluxation, dislocation, and revision surgery. Results: Sixteen categories represented at least one percent of patients. Occasional Traumatic Anterior Dislocation (OTAD) was the most common category with 16.4% of patients. Five other anterior categories (STAS, OTAS, FTAS, STAD, FTAD) and one posterior category (STPS) represented at least 5%. PROs and failure rates for anterior categories are summarized in Figure 1. PROs improved significantly for each category. A downward trend in WOSI and ASES was noted in particular with increasing frequency of the dislocation groups. The highest rates of each type of failure occurred in the occasional and frequent groups for both dislocation and subluxation. Low rates of failure occurred in STPS, with 17.9% reporting subluxation, 3.6% dislocation, and no revisions. Conclusion: While overall success was good, different FEDS categories showed varying degrees of improvement and failure rates, indicating that the system can be used to provide prognostic insight for presurgical education. Overall, outcomes for traumatic anterior instability decreased with higher initial frequency, showing worse PROs and higher failure. Frequency appeared to have the greatest effect on outcomes. Early surgical intervention may be beneficial in preventing progression to more severe FEDS categories, with higher frequency having previously been associated with both higher rates of bone loss and greater time between initial event and surgical stabilization.

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)0.002
metaresearch head score (Gemma)0.006
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 score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.049
GPT teacher head0.330
Teacher spread0.281 · 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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Citations1
Published2020
Admission routes1
Has abstractyes

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