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Record W2736641438

EVALUATION OF CONVENTIONAL AND PATIENT-SPECIFIC INSTRUMENTATION IN TOTAL KNEE REPLACEMENT USING RADIOSTEREOMETRIC ANALYSIS

2018· article· en· W2736641438 on OpenAlexaff
Matthew G. Teeter, Edward M. Vasarhelyi, Xunhua Yuan, Richard W. McCalden, Douglas D.R. Naudie

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldArts and Humanities
TopicHermeneutics and Narrative Identity
Canadian institutionsLawson Health Research Institute
Fundersnot available
KeywordsWOMACMedicineImplantTotal knee replacementFemurSupine positionSurgeryInstrumentation (computer programming)Knee replacementOsteoarthritisOrthopedic surgeryComputer science
DOInot available

Abstract

fetched live from OpenAlex

Background Patient specific instrumentation (PSI) for total knee replacement (TKR) has demonstrated mixed success in simplifying the operation, reducing its costs, and improving limb alignment. Evaluation of PSI with tools such as radiostereometric analysis (RSA) has been limited, especially for cut-through style guides providing mechanical alignment. The primary goal of the present study was to compare implant migration following TKR using conventional and PSI surgical techniques, with secondary goals to examine whether the use of PSI reduces operative time, instrumentation, and surgical waste. Methods The study was designed as a prospective, randomized controlled trial of 50 patients, with 25 patients each in the PSI and conventional groups, powered for the RSA analysis. Patients in the PSI group received an MRI and standing 3-foot x-rays to construct patient-specific cut-through surgical guides for the femur and tibia with a mechanical alignment. All patients received the same posterior-stabilized implant, with marker beads inserted in the bone around the implants to enable RSA imaging. Intraoperative variables such as time, number of instrumentation trays used, and mass of surgical waste were recorded. Patients underwent supine RSA exams at multiple time points (2&6 weeks, 3&6 months and yearly) with 6 months data currently available. Migration of the tibial and femoral components was calculated using model-based RSA software. WOMAC, SF-12, EQ5D, and UCLA outcome measures were recorded pre-operatively and post-operatively. Results There were no demographic differences between groups. One patient in the PSI group was revised for infection, and three patients required manipulation, with no revisions or manipulations in the conventional group. There was no difference in maximum total point motion between groups for the tibia (mean 0.50 vs. 0.50 mm, p = 0.98) or femur (mean 0.46 vs. 0.48 mm, p = 0.87). The PSI group displayed greater tibial posterior tilt (p = 0.048, Fig. 1) and greater femoral anterior tilt (p = 0.01) and valgus rotation (p = 0.04, Fig. 2) than the conventional group, but there were no other differences in migrations. The PSI group required less instrument trays than the conventional group (mean 4.8 vs. 8.1 trays, p Discussion At early RSA follow-up, the two groups were broadly similar in implant fixation except for small rotational changes in the tibial and femoral components. The PSI group provided minimal or no advantage over the conventional group for operative time, instrumentation used, or surgical waste produced. The observed increase in manipulations in the PSI group is concerning, and requires additional investigation. Further radiographic and economic analysis is underway to determine if there is any benefit to the use of PSI for TKR during the perioperative and early follow-up period. For any figures or tables, please contact authors directly (see Info & Metrics tab above).

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)0.004
metaresearch head score (Gemma)0.004
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 score0.004
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.081
GPT teacher head0.302
Teacher spread0.221 · 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".

Quick stats

Citations0
Published2018
Admission routes1
Has abstractyes

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