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Record W2041164618 · doi:10.1016/j.otohns.2008.12.049

Gamma tubulin: A promising indicator of recurrence in squamous cell carcinoma of the larynx

2009· article· en· W2041164618 on OpenAlexaff
Mohammed Iqbal Syed, Sheeba Syed, Fay Minty, Steven Harrower, Jatinder Singh, Andy Chin, Douglas McLellan, Eric Kenneth Parkinson, Louise Clark

Bibliographic record

VenueOtolaryngology · 2009
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMicrotubule and mitosis dynamics
Canadian institutionsBioinformatics Solutions (Canada)
Fundersnot available
KeywordsImmunostainingPathologyAntigenKeratinStainingLarynxCarcinomaImmunohistochemistryAntibodyStage (stratigraphy)Basal cellMedicineCancer researchBiologyImmunology

Abstract

fetched live from OpenAlex

OBJECTIVE: Centrosome amplification as detected by gamma tubulin (GT) immunostaining is associated with genetic instability and tumor aggressiveness. We assessed GT for its ability to predict recurrence of squamous cell carcinoma of the larynx (SCCL). STUDY DESIGN: Case series with chart review. MATERIALS AND METHODS: Five micron sections of 35 archival SCCL samples were subjected to antigen retrieval and immunostaining with antibody to GT. The keratin antibody CK5 served as a positive control for antigen retrieval, and tonsillar tissue was used as a negative control. RESULTS: Of the 35 tumors analyzed, 22 were associated with recurrence(R) and 13 were not (NR). Fourteen of the 22 R tumors, but 0 of 13 of the NR tumours had a GT staining score of 2+ or 3+ (P < 0.0002). GT was also related to recurrence in node-negative tumors (P < 0.006) but was unrelated to T stage (P = 0.726). CONCLUSIONS: GT staining appears to be a better predictor of tumor recurrence than T stage and also predicts recurrence in N0 tumors.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.121
Threshold uncertainty score0.406

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0000.000

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 head0.006
GPT teacher head0.221
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 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".

Quick stats

Citations11
Published2009
Admission routes1
Has abstractyes

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