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Record W3118750144 · doi:10.1111/os.12895

Early Patellofemoral Function of Medial Pivot Prostheses Compared with <scp>Posterior‐Stabilized</scp> Prostheses for Unilateral Total Knee Arthroplasty

2021· article· en· W3118750144 on OpenAlexaboutno aff
Zhen Wang, Yuqing Zhang, Changrong Ding, Y Z Wang, Hao Xu

Bibliographic record

VenueOrthopaedic Surgery · 2021
Typearticle
Languageen
FieldMedicine
TopicTotal Knee Arthroplasty Outcomes
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineWOMACProsthesisOsteoarthritisRange of motionRadiographyArthroplastyTotal knee arthroplastyPatellaCondyleSurgeryBody mass index

Abstract

fetched live from OpenAlex

OBJECTIVE: To systematically evaluate the patellofemoral joint design of medial pivot prosthesis, which incorporates a variety of "patella-friendly" design features, by comparing clinical and radiographic results with another prosthesis. METHODS: All consecutive patients who underwent unilateral total knee arthroplasty (TKA) with medial pivot prosthesis (Group MP, 126 cases) between September 2016 and April 2018 were enrolled in this retrospective study. For each patient reviewed, a control patient was matched, according to age, gender, side, body mass index (BMI), preoperative range of motion (ROM), and operating period, who had received primary unilateral TKA with a conventional posterior-stabilized prosthesis at the same period as the study group (Group PS, 126 cases). All patients underwent at least 1-year follow-up. At the preoperative and final follow-up periods, data on the Knee Society Score (KSS) score, WOMAC score, Kujala score, and ROM were collected. Merchant views were taken with the knee flexion at 30°, 60°, and 90° to measure patella shift and tilt. Preoperative posterior condylar angle (PCA) was also measured. Postoperative complications, including anterior knee pain, maltracking, patellar clunk or crepitus (PCC), were evaluated. RESULTS: There were no significant differences in the demographics or clinical characteristics between the two groups. No statistically significant difference was identified in the KSS total score, including knee score and function score, or in the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) score between the two groups after the operation. We found statistically significant differences in the postoperative Kujala scores and the ROMs between the two groups. The mean Kujala score in group MP was better than in group PS (MP 77.16 ± 3.80 vs PS 75.97 ± 4.06, P < 0.05), while the ROM in group PS was significantly higher than in group MP (MP 122.24° ± 4.45° vs PS 123.78° ± 6.05°, P < 0.05). Simultaneously, the preoperative/postoperative Kujala score improvement in group MP was observed to be significantly larger than in group PS (MP 27.82 ± 5.31 vs PS 26.17 ± 4.89, P < 0.05), but the average ROM improvement in group PS was significantly greater than in group MP (MP 19.00° ±9.90° vs PS 21.57° ± 9.62°). In the 90° Merchant view, the mean patella tilt of group MP was statistically smaller than that of group PS (MP 4.21° ± 1.62° vs PS 4.74° ± 1.95°, P < 0.05), and the average patella tilt change in group MP was significantly greater than in group PS (MP -3.8° ± 1.43° vs PS -3.23° ± 1.33°, P < 0.05). Preoperative PCA did not show significant differences between the two groups. Two cases of PCC and three cases of anterior knee pain were noted in group MP, and nine cases and six cases, respectively, were observed in group PS. The incidence of PCC was significantly lower in group MP (1.6% vs 7.1%, P < 0.05). There was no significant difference in follow-up time between the two groups. CONCLUSION: The medial pivot prosthesis could achieve satisfactory outcomes with better patellofemoral performance attributed to its "patella-friendly" design characteristics compared to the conventional posterior-stabilized 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)0.000
metaresearch head score (Gemma)0.002
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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.023
GPT teacher head0.238
Teacher spread0.215 · 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

Citations13
Published2021
Admission routes1
Has abstractyes

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