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Record W2994762691 · doi:10.1503/cjs.018318

A double-blind randomized controlled trial of total knee replacement using patient-specific cutting block instrumentation versus standard instrumentation

2019· article· en· W2994762691 on OpenAlexaffvenue
Thomas R. Turgeon, Brett R. Cameron, Colin D. Burnell, David R. Hedden, Éric Bohm

Bibliographic record

VenueCanadian Journal of Surgery · 2019
Typearticle
Languageen
FieldMedicine
TopicTotal Knee Arthroplasty Outcomes
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsMedicineInstrumentation (computer programming)Total knee replacementDouble blindRandomized controlled trialBlock (permutation group theory)Physical therapyMedical physicsSurgeryPathology

Abstract

fetched live from OpenAlex

Background: Patient-specific cutting blocks in total knee arthroplasty have been promoted to improve mechanical alignment, reduce alignment outliers and improve patient outcomes. The aim of this study was to compare the efficacy of patient-specific instrumentation (PSI) and conventional instrumentation (CI) in achieving neutral alignment and accurate component positioning in total knee arthroplasty. Methods: We conducted a double-blinded randomized controlled trial in which patients were randomly assigned to treatment with either PSI or CI. Results: Fifty-four patients were included in the study. No relevant improvement in coronal alignment was found between the PSI and CI groups with post-hoc power of 0.91. Tibial slope was found to be more accurately reproduced to the preoperative target of 3° with PSI than with CI (3.8°± 3.1° v. 7.7°± 3.6°, respectively, p < 0.001). There were no differences found in patient-reported outcome measures, surgical time or length of hospital stay. Conclusion: Given the added cost of the PSI technique, its use is difficult to justify given the small improvement in only a single alignment parameter. Clinical trial registration: Clinicaltrials.gov, no. NCT03416946

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.692

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.041
GPT teacher head0.282
Teacher spread0.241 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designRandomized trial
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

Citations16
Published2019
Admission routes2
Has abstractyes

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