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Record W2516731016 · doi:10.1177/1071100716659037

A Coding System for Reoperations Following Total Ankle Replacement and Ankle Arthrodesis

2016· article· en· W2516731016 on OpenAlexaff
Alastair Younger, Mark Glazebrook, Andrea Veljkovic, Gordon Goplen, Timothy R. Daniels, Murray J. Penner, Kevin Wing, Peter Dryden, Hubert Wong, Karl‐André Lalonde

Bibliographic record

VenueFoot & Ankle International · 2016
Typearticle
Languageen
FieldHealth Professions
TopicMedical Malpractice and Liability Issues
Canadian institutionsUniversity of OttawaQueen Elizabeth II Health Sciences CentreUniversity of AlbertaUniversity of TorontoDalhousie UniversityIsland HealthUniversity of British Columbia
Fundersnot available
KeywordsMedicineAnkleAnkle replacementArthrodesisSurgeryAnkle arthrodesisIntraclass correlation

Abstract

fetched live from OpenAlex

BACKGROUND: Repeated surgery can be a measure of failure of the primary surgery. Future reoperations might be avoided if the cause is recognized and procedures or devices modified accordingly. Reoperations result in costs to both patient and the health care system. This paper proposes a new classification system for reoperations in end-stage ankle arthritis, and analyzes reoperation rates for ankle joint replacement and arthrodesis surgeries from a multicenter database. METHODS: A total of 213 ankle arthrodeses and 474 total ankle replacements were prospectively followed from 2002 to 2010. Reoperations were identified as part of the prospective cohort study. Operating reports were reviewed, and each reoperation was coded. To verify inter- and intraobserver reliability of this new coding system, 6 surgeons experienced in foot and ankle surgery were asked to assign a specific code to 62 blinded reoperations, on 2 separate occasions. Reliability was determined using intraclass correlation coefficients (ICCs) and proportions of agreement. RESULTS: Of a total of 687 procedures, 74.8% (514/687) required no reoperation (Code 1). By surgery type, 14.1% (30/213) of ankle arthrodesis procedures and 30.2% (143/474) of ankle replacement procedures required reoperation. The rate for reoperations surrounding the ankle joint (ie, Codes 2 and 3) was 9.9% (21/213) for ankle arthrodesis versus 5.9% for ankle replacement (28/474). Reoperation rates within the ankle joint (ie, Codes 4 to 10) were 4.7% (10/213) for ankle arthrodesis and 26.1% (124/474) for ankle replacement. Overall, 0.9% (2/213) of arthrodesis procedures required reoperation outside the initial operative site (Code 3), versus 4.6% (22/474) for total ankle replacement. The rate of reoperation due to deep infection (Code 7) was 0.9% (2/213) for arthrodesis versus 2.3% (11/474) for ankle replacement. Interobserver reliability testing produced a mean ICC of 0.89 on the first read. The mean ICC for intraobserver reliability was 0.92. For interobserver, there was 87.9% agreement (804/915) on the first read, and 87.5% agreement (801/915) on the second. For the intra observer readings, 88.5% (324/366) were in agreement. CONCLUSIONS: The new coding system presented here was reliable and may provide a more standardized, clinically useful framework for assessing reoperation rates and resource utilization than prior complication- and diagnosis-based classification systems, such as modifications of the Clavien Dindo System. Analyzing reoperations at the primary site may enable a better understanding of reasons for failure, and may therefore improve the outcomes of surgery in the future. LEVEL OF EVIDENCE: Level III, retrospective comparative cohort study based on prospectively collected data.

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)0.010
metaresearch head score (Gemma)0.028
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.012
Threshold uncertainty score0.051

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.028
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0070.004
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.057
GPT teacher head0.406
Teacher spread0.349 · 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 designNot applicable
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

Citations62
Published2016
Admission routes1
Has abstractyes

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