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Record W2418625208 · doi:10.1007/s11999-016-4898-9

No Difference Between Trabecular Metal Cones and Femoral Head Allografts in Revision TKA: Minimum 5-year Followup

2016· article· en· W2418625208 on OpenAlexaff
Nemandra A. Sandiford, Peter Misur, Donald S. Garbuz, Nelson V. Greidanus, Bassam A. Masri

Bibliographic record

VenueClinical Orthopaedics and Related Research · 2016
Typearticle
Languageen
FieldMedicine
TopicTotal Knee Arthroplasty Outcomes
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMedicineFemoral headWOMACSurgeryOrthopedic surgeryRadiographyFemurTibiaProsthesisTrabecular boneOsteoarthritisOsteoporosisInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Encouraging clinical results have been reported with the use of femoral head structural allografts and, more recently, trabecular metal cones for the management of large structural defects of the femur and tibia during revision total knee arthroplasty (TKA). However, to our knowledge, there are no published studies comparing these two techniques. QUESTIONS/PURPOSES: Compared with bulk allografts, do trabecular metal cones result in (1) better validated outcomes scores; (2) a lower risk of loosening or revision at 5 years; and (3) fewer surgical complications when used for the management of bone loss in revision TKA? METHODS: Between 2002 and 2008, three surgeons performed 450 TKA revisions, 45 (10%) of which were performed using augmentation of host bone; in those, femoral head allograft was used in 30 (75%) and trabecular metal cones in 15 (25%). From 2002 to 2007, femoral head allografts were used in all patients (28 patients); from 2007 to 2008, trabecular metal augments were used in all patients. There was a period of 1 year (16 knees) in which there was some overlap; during that time, femoral head structural allografts were used in cases in which we were unable to fit the defect or achieve adequate stability with trabecular metal cones. Followup was at a mean of 9 years (range, 5-12 years). No patients were lost to followup. Knee function and quality of life were assessed using the Oxford Knee Score, WOMAC, SF-12, and the UCLA activity score. Radiographs were assessed for signs of loosening. Surgical complications included superficial or deep infections, iatrogenic fractures, symptomatic deep venous thromboses or pulmonary emboli, and blood loss requiring transfusion; these were obtained from our database and from review of patients' charts. RESULTS: The mean Oxford Knee Score in the allograft and trabecular metal cone groups was 91 (SD 10) and 91 (SD 14), respectively (95% confidence interval [CI], 88-94; p = 0.29). Mean WOMAC scores were 94 (SD 10) and 92 (SD 14), respectively (95% CI, 80-105; p = 0.52) and mean UCLA scores were 6 (SD 1.2) and 6 (SD 1.5), respectively (95% CI, 4-8; p = 0.49). Five- and 10-year survivorship of the allografts was 93% (95% CI, 77-98) and 93% (95% CI, 77-99), respectively. Survivorship at a mean of 5 years in the trabecular metal cones group was 91% (95% CI, 56-98). With the numbers available, there were no differences between the groups in terms of the frequency of surgical complications (3% [one of 30] versus 7% [one of 15]; odds ratio, 0.5; p = 0.632). CONCLUSIONS: With the numbers available, we found no difference in pain, function, or repeat revision when comparing femoral head allografts and trabecular metal cones for severe bone defects during revision TKA. However, we used allografts for the larger bone defects. Based on these results, we believe that femoral head allografts and trabecular metal cones can both be used for the management of Anderson Orthopaedic Research Institute Types 2 and 3 defects. Future multicenter studies are required with larger numbers, cost analyses, and a longer duration of followup. LEVEL OF EVIDENCE: Level III, therapeutic study.

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)0.004
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation 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 score0.071
Threshold uncertainty score0.567

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.100
GPT teacher head0.423
Teacher spread0.323 · 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 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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Citations66
Published2016
Admission routes1
Has abstractyes

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