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Record W4399177655 · doi:10.1097/corr.0000000000003116

Does Freehand, Patient-specific Instrumentation or Surgical Navigation Perform Better for Allograft Reconstruction After Tumor Resection? A Preclinical Synthetic Bone Study

2024· article· en· W4399177655 on OpenAlexafffund
Harley Chan, Prakash Nayak, Ibrahim Alshaygy, Kenneth R. Gundle, Kim M. Tsoi, Michael J. Daly, Jonathan C. Irish, Peter C. Ferguson, Jay S. Wunder

Bibliographic record

VenueClinical Orthopaedics and Related Research · 2024
Typearticle
Languageen
FieldMedicine
TopicSarcoma Diagnosis and Treatment
Canadian institutionsUniversity of TorontoMount Sinai HospitalPrincess Margaret Cancer CentreUniversity Health Network
FundersPrincess Margaret Cancer Foundation
KeywordsMedicineResectionInstrumentation (computer programming)Surgical resectionSurgeryBone transplantationMedical physics

Abstract

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BACKGROUND: Joint-sparing resection of periarticular bone tumors can be challenging because of complex geometry. Successful reconstruction of periarticular bone defects after tumor resection is often performed with structural allografts to allow for joint preservation. However, achieving a size-matched allograft to fill the defect can be challenging because allograft sizes vary, they do not always match a patient's anatomy, and cutting the allograft to perfectly fit the defect is demanding. QUESTIONS/PURPOSES: (1) Is there a difference in mental workload among the freehand, patient-specific instrumentation, and surgical navigation approaches? (2) Is there a difference in conformance (quantitative measure of deviation from the ideal bone graft), elapsed time during reconstruction, and qualitative assessment of goodness-of-fit of the allograft reconstruction among the approaches? METHODS: Seven surgeons used three modalities in the same order (freehand, patient-specific instrumentation, and surgical navigation) to fashion synthetic bone to reconstruct a standardized bone defect. National Aeronautics and Space Administration (NASA) mental task load index questionnaires and procedure time were captured. Cone-beam CT images of the shaped allografts were used to measure conformance (quantitative measure of deviation from the ideal bone graft) to a computer-generated ideal bone graft model. Six additional (senior) surgeons blinded to modality scored the quality of fit of the allografts into the standardized tumor defect using a 10-point Likert scale. We measured conformance using the root-mean-square metric in mm and used ANOVA for multipaired comparisons (p < 0.05 was significant). RESULTS: There was no difference in mental NASA total task load scores among the freehand, patient-specific instrumentation, and surgical navigation techniques. We found no difference in conformance root-mean-square values (mean ± SD) between surgical navigation (2 ± 0 mm; mean values have been rounded to whole numbers) and patient-specific instrumentation (2 ± 1 mm), but both showed a small improvement compared with the freehand approach (3 ± 1 mm). For freehand versus surgical navigation, the mean difference was 1 mm (95% confidence interval [CI] 0.5 to 1.1; p = 0.01). For freehand versus patient-specific instrumentation, the mean difference was 1 mm (95% CI -0.1 to 0.9; p = 0.02). For patient-specific instrumentation versus surgical navigation, the mean difference was 0 mm (95% CI -0.5 to 0.2; p = 0.82). In evaluating the goodness of fit of the shaped grafts, we found no clinically important difference between surgical navigation (median [IQR] 7 [6 to 8]) and patient-specific instrumentation (median 6 [5 to 7.8]), although both techniques had higher scores than the freehand technique did (median 3 [2 to 4]). For freehand versus surgical navigation, the difference of medians was 4 (p < 0.001). For freehand versus patient-specific instrumentation, the difference of medians was 3 (p < 0.001). For patient-specific instrumentation versus surgical navigation, the difference of medians was 1 (p = 0.03). The mean ± procedural times for freehand was 16 ± 10 minutes, patient-specific instrumentation was 14 ± 9 minutes, and surgical navigation techniques was 24 ± 8 minutes. We found no differences in procedure times across three shaping modalities (freehand versus patient-specific instrumentation: mean difference 2 minutes [95% CI 0 to 7]; p = 0.92; freehand versus surgical navigation: mean difference 8 minutes [95% CI 0 to 20]; p = 0.23; patient-specific instrumentation versus surgical navigation: mean difference 10 minutes [95% CI 1 to 19]; p = 0.12). CONCLUSION: Based on surgical simulation to reconstruct a standardized periarticular bone defect after tumor resection, we found a possible small advantage to surgical navigation over patient-specific instrumentation based on qualitative fit, but both techniques provided slightly better conformance of the shaped graft for fit into the standardized post-tumor resection bone defect than the freehand technique did. To determine whether these differences are clinically meaningful requires further study. The surgical navigation system presented here is a product of laboratory research development, and although not ready to be widely deployed for clinical practice, it is currently being used in a research operating room setting for patient care. This new technology is associated with a learning curve, capital costs, and potential risk. The reported preliminary results are based on a preclinical synthetic bone tumor study, which is not as realistic as actual surgical scenarios. CLINICAL RELEVANCE: Surgical navigation systems are an emerging technology in orthopaedic and reconstruction surgery, and understanding their capabilities and limitations is paramount for clinical practice. Given our preliminary findings in a small cohort study with one scenario of standardized synthetic periarticular bone tumor defects, future investigations should include different surgical scenarios using allograft and cadaveric specimens in a more realistic surgical setting.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.069
GPT teacher head0.420
Teacher spread0.352 · 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 designBench or experimental
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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Citations4
Published2024
Admission routes2
Has abstractyes

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