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Record W2755970561 · doi:10.1177/2473011417s000403

Preoperative Patient-reported Outcome Measures (PROMs) Predict Postoperative Success in Patients With End-Stage Ankle Arthritis

2017· article· en· W2755970561 on OpenAlexaboutno aff
Feras Waly, Kevin Wing, Murray J. Penner, Andrea Veljkovic, Alastair Younger

Bibliographic record

VenueFoot & Ankle Orthopaedics · 2017
Typearticle
Languageen
FieldMedicine
TopicFoot and Ankle Surgery
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineAnkleAnkle replacementPromMinimal clinically important differencePhysical therapyArthritisLogistic regressionPatient-reported outcomeReceiver operating characteristicStage (stratigraphy)OsteoarthritisSurgeryQuality of life (healthcare)Internal medicineRandomized controlled trial

Abstract

fetched live from OpenAlex

Category: Ankle, Ankle Arthritis Introduction/Purpose: Ankle arthritis is a leading cause of pain and disability. Total ankle replacement and Ankle fusion are two common surgical treatments with successful outcomes. Despite the effectiveness of those treatments, a subset of patients remains with persistent pain and functional limitations. This has prompted the search for predictive tools capable of identifying patients who are likely to benefit from surgery which allows surgeons to provide valuable prognostic information, implement, and direct appropriate treatment programs. The purpose of this study is to use preoperative patient-reported outcome measures (PROMs) to predict which patients with end-stage ankle arthritis undergoing surgical treatment are most likely to experience postoperative improvements (a clinically meaningful change) in functional outcomes at an average follow-up of five years after surgical treatment. Methods: A prospective cohort design used to evaluate 427 Patients with end-stage ankle arthritis at preoperative baseline and an average of five years after undergoing total ankle replacement or ankle fusion at a single academic institution. Data on demographics, comorbidities, Ankle Osteoarthritis Score (AOS), and Physical components (PCS) of SF-36 were collected. The Canadian Orthopaedic Foot and Ankle Society Ankle Arthritis Score (COFAS-AAS) was calculated from the AOS. The Minimal clinically important difference (MCID) was then determined using a distribution-based method. A multivariable logistic regression analysis examined the variables affecting the change in PROM scores. Receiver operating characteristic (ROC) analysis was used to calculate threshold values, defined as the levels at which substantial changes occurred, and their predictive ability to determine whether preoperative PROM scores were predictive of achieving MCID. Results: Patients who scored worst at preoperative baseline made the greatest gains in function and pain relief following surgical treatment. ROC curves demonstrated that preoperative AOS, COFAS-AAS, SF-36 PCS physical function scores were predictive of postoperative improvements in physical function. Patients with preoperative AOS score above 45.7 had an 83% probability of achieving a clinically meaningful improvement in function as defined by MCID (area under the curve [AUC] 0.67). Similarly, Patients with preoperative COFAS-AAS score above 25.70 had a 78% probability of achieving MCID (area under the curve [AUC] 0.63). Patients with preoperative SF-36 PCS score below 31 had a 62% probability of achieving MCID (area under the curve [AUC] 0.64). MCIDs for AOS, COFAS-AAS and SF3-36 PCS score changes were 12.35, 9.99 and 6.43, respectively. Conclusion: In the present study, we identified PROM threshold values that predict clinically meaningful improvements in functional outcome in patients with end-stage ankle OA. Patients with a higher level of preoperative function are less likely to obtain meaningful improvement after surgical treatment. The results of this study may be used to facilitate discussion between physicians and patients regarding the expected functional benefit after surgery and to support the development of patient-based informed decision-making tools.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.024
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.027
GPT teacher head0.270
Teacher spread0.243 · 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

Citations0
Published2017
Admission routes1
Has abstractyes

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