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Record W2565302917

KS36: WHICH HIGH-DEMAND ACTIVITIES SHOULD BE CONSIDERED IN THE EVALUATION OF FUNCTION AFTER TKA?

2010· article· en· W2565302917 on OpenAlexaboutno aff
Philip C. Noble, Michael A. Conditt, Jennifer M. Weiss, Kenneth B. Mathis, Brian S. Parsley

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMedicine
TopicTotal Knee Arthroplasty Outcomes
Canadian institutionsnot available
Fundersnot available
KeywordsPhysical therapyRespondentKnee replacementRecreationMedicineActivities of daily livingArthroplastyPhysical activityPhysical medicine and rehabilitationSurgery
DOInot available

Abstract

fetched live from OpenAlex

Introduction: It is generally agreed that the function of the knee after total knee arthroplasty needs to be improved to meet the expectations of younger and more active patients. However, little objective information consists to quantify the frequency and importance of activities that place increased biomechanical demand on the knee. This study was performed to asses which specific “high-demand” activities are actually performed by patients after knee replacement, and which activities are of greatest personal importance to the patient. Methods: An initial group of 243 patients (47% male; 53% female, average age: 70 years; range: (45–91 yrs)) were enrolled in this study with Institutional approval. All were at least 1 year post knee replacement and resided in the Houston area. All participants completed a validated, self-administered knee function questionnaire consisting of 55 scaled multiple choice questions regarding each respondent’s physical activities, limitations, and level of importance for those activities. Participants were also asked to assess the personal importance of each activity and the severity of any symptoms experienced when each activity was performed. An expanded version of the Knee Function Questionnaire was completed by a second group of 101 patients from 5 centres in the United States and Canada. This instrument addressed 120 physical, vocational and recreational activities involving the knee. Fifty-four of these activities were considered “highly demanding” and were drawn from a wide variety of water and team sports, martial arts, running/biking, exercise, weight-lifting and fitness training. Results: The initial study demonstrated that TKR patients participate in a wide range of “high demand” activities. Most commonly, activities requiring increased knee flexion were gardening (58% participation), kneeling (64%), and squatting (39%). Moderate to severe difficulty was reported by 39% (squatting) to 64% (kneeling) of respondents performing these activities. The most common activity which placed increased loads on the affected joint was carrying loaded shopping bags (47% participation), which provoked Significant symptoms in 23% of patients. The expanded nation-wide study showed that after TKR, few patients actually perform high impact competitive sports although many patients perform individual exercise routines which potentially place Significant demands on the knee. The most common of these “high demand” activities were still squatting and kneeling, but also included participation in gym and exercise activities, typically leg extensions (59%), leg curls (35%) and leg press exercises (33%). Conclusions: Kneeling and squatting are the most common “high-demand” activities actually performed on a routine basis by patients after TKR After TKR, patients rarely participate in particularly demanding competitive sports, however, individualized exercise and fitness activities are common. As these activities vary extensively, surgeons are advised to ask individual patients which activities they enjoy for recreation and exercise to enable specific advice to be provided concerning possible impact on the durability of the prosthesis.

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)0.001
metaresearch head score (Gemma)0.003
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 score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.000
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 head0.049
GPT teacher head0.318
Teacher spread0.269 · 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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Citations0
Published2010
Admission routes1
Has abstractyes

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