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Record W2046870500 · doi:10.2106/jbjs.k.00971

Effects of Neoadjuvant Chemotherapy on Image-Directed Planning of Surgical Resection for Distal Femoral Osteosarcoma

2012· article· en· W2046870500 on OpenAlexaff
Kevin B. Jones, Peter C. Ferguson, Brian Lam, David Biau, Sevan Hopyan, Benjamin Deheshi, Anthony M. Griffin, Lawrence M. White, Jay S. Wunder

Bibliographic record

VenueJournal of Bone and Joint Surgery · 2012
Typearticle
Languageen
FieldMedicine
TopicSarcoma Diagnosis and Treatment
Canadian institutionsHospital for Sick ChildrenMount Sinai Hospital
FundersNational Cancer Institute
KeywordsMedicineIntraclass correlationChemotherapyOsteosarcomaMagnetic resonance imagingRadiologySurgeryNeoadjuvant therapyCancerBreast cancerInternal medicinePathology

Abstract

fetched live from OpenAlex

BACKGROUND: Standard therapy for localized osteosarcoma includes neoadjuvant chemotherapy preceding local control surgery, followed by adjuvant chemotherapy. When limb-salvage procedures were being developed, preoperative chemotherapy allowed a delay in definitive surgery to permit fabrication of custom endoprosthetic reconstruction implants. One rationale for its continuation as the care standard has been the perception that it renders surgery easier and safer. Our objective was to compare surgical procedures planned on the basis of magnetic resonance images (MRIs) of distal femoral osteosarcomas acquired before neoadjuvant chemotherapy with surgical procedures planned on the basis of MRIs acquired after neoadjuvant chemotherapy as a measure of the surgically critical anatomic effects of the chemotherapy. METHODS: Twenty-four consecutive patients with distal femoral osteosarcoma had available digital MRIs preceding and following neoadjuvant chemotherapy. Thorough questionnaires were used to catalogue surgically critical anatomic details of MRI-directed surgical planning. Four faculty musculoskeletal oncologic surgeons and two musculoskeletal radiologists evaluated the blinded and randomly ordered MRIs. Interrater and intrarater reliabilities were calculated with intraclass correlation coefficients. The Student t test and chi-square test were used to compare pre-chemotherapy and post-chemotherapy continuous and categorical variables on the questionnaire. Mixed-effect regression models were employed to compare surgical procedures planned on the basis of pre-chemotherapy MRIs and with those planned on the basis of post-chemotherapy MRIs. RESULTS: The blinded reviews generated strong intraclass correlation coefficients for both interrater (0.772) and mean intrarater (0.778) reliability. The MRI-planned resections for the majority of tumors changed meaningfully after chemotherapy, but in inconsistent directions. On the basis of mixed-effect regression modeling, it appeared that more amputations were planned on the basis of post-chemotherapy MRIs. No other parameters differed in a significant and clinically meaningful fashion. Surgeons demonstrated their expectation that neoadjuvant chemotherapy would improve resectability by planning more radical surgical procedures on the basis of scans that they predicted had been obtained pre-chemotherapy. CONCLUSIONS: Surgeons can reliably record the anatomic details of a planned resection of an osteosarcoma. Such methods may be useful in future multi-institutional clinical trials or registries. The common belief that neoadjuvant chemotherapy increases the resectability of extremity osteosarcomas remains anecdotally based. Rigorous assessment of this phenomenon in larger cohorts and at other anatomic sites as well as re-evaluation of other arguments for neoadjuvant chemotherapy should be considered.

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)0.005
metaresearch head score (Gemma)0.046
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 score0.005
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.046
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 head0.027
GPT teacher head0.291
Teacher spread0.265 · 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

Citations41
Published2012
Admission routes1
Has abstractyes

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