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Record W2252604341 · doi:10.1302/0301-620x.92b10.23495

The efficacy of chemical adjuvants on giant-cell tumour of bone

2010· article· en· W2252604341 on OpenAlexaff
Y. Gortzak, Rita A. Kandel, Benjamin Deheshi, Joel Werier, Robert Turcotte, Peter C. Ferguson, Jay S. Wunder

Bibliographic record

VenueJournal of Bone and Joint Surgery - British Volume · 2010
Typearticle
Languageen
FieldMedicine
TopicBone Tumor Diagnosis and Treatments
Canadian institutionsMount Sinai HospitalOttawa HospitalMontreal General HospitalUniversity of Toronto
Fundersnot available
KeywordsHydrogen peroxideAdjuvantViability assayCytotoxicityCytotoxic T cellGiant cellChemistryPhenolEthanolCellBiochemistryMedicineImmunologyPathologyIn vitroOrganic chemistry

Abstract

fetched live from OpenAlex

Various chemicals are commonly used as adjuvant treatment to surgery for giant-cell tumour (GCT) of bone. The comparative effect of these solutions on the cells of GCT is not known. In this study we evaluated the cytotoxic effect of sterile water, 95% ethanol, 5% phenol, 3% hydrogen peroxide (H(2)O(2)) and 50% zinc chloride (ZnCI(2)) on GCT monolayer tumour cultures which were established from six patients. The DNA content, the metabolic activity and the viability of the cultured samples of tumour cells were assessed at various times up to 120 hours after their exposure to these solutions. Equal cytotoxicity to the GCT monolayer culture was observed for 95% ethanol, 5% phenol, 3% H(2)O(2) and 50% ZnCI(2). The treated samples showed significant reductions in DNA content and metabolic activity 24 hours after treatment and this was sustained for up to 120 hours. The samples treated with sterile water showed an initial decline in DNA content and viability 24 hours after treatment, but the surviving cells were viable and had proliferated. No multinucleated cell formation was seen in these cultures. These results suggest that the use of chemical adjuvants other than water could help improve local control in the treatment of GCT of bone.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.658
Threshold uncertainty score0.497

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
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)0.0000.000

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 head0.013
GPT teacher head0.227
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 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".

Quick stats

Citations54
Published2010
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Bone and Joint Surgery - British VolumeSame topicBone Tumor Diagnosis and TreatmentsFrench-language works237,207