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Record W2527422888 · doi:10.1093/ajcp/aqw141

Phosphohistone-H3 Proliferation Index Is Superior to Mitotic Index and MIB-1 Expression as a Predictor of Recurrence in Human Meningiomas

2016· article· en· W2527422888 on OpenAlexaff
Theo L. Winther, Magnus Arnli, Øyvind Salvesen, Sverre H. Torp

Bibliographic record

VenueAmerican Journal of Clinical Pathology · 2016
Typearticle
Languageen
FieldMedicine
TopicMeningioma and schwannoma management
Canadian institutionsChildren's & Women's Health Centre of British Columbia
Fundersnot available
KeywordsMitotic indexImmunohistochemistryUnivariateTissue microarrayProliferation indexBiomarkerProportional hazards modelUnivariate analysisMedicinePathologyMultivariate analysisInternal medicineProliferative indexOncologyMultivariate statisticsBiologyMitosisStatisticsMathematics

Abstract

fetched live from OpenAlex

OBJECTIVES: This study investigated the prognostic value of the phosphohistone-H3 (PHH3) proliferation index (PI) in human meningiomas and compared the reliability with the conventional mitotic index and MIB-1 biomarker. METHODS: Proliferative activity was determined in 160 patients by standardized immunohistochemistry on tissue microarrays and related to recurrence. RESULTS: All three proliferation assessment methods were significantly associated with World Health Organization grade. The optimal cutoff values for recurrence prediction were 3% for the MIB-1 PI and 0.5% for the PHH3 PI. Increased PHH3 PI was significantly associated with recurrence-free survival in univariate Cox proportional hazards regression analysis (P = .011) and remained an independent predictor in multivariate analysis (P = .005). Mitotic index and MIB-1 PI did not reach statistical significance. CONCLUSIONS: PHH3 immunostaining allowed for the easiest, fastest, and most objective assessment of proliferation and proved to be the most accurate and reliable method for predicting recurrence in patients resected for meningiomas.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.071
Threshold uncertainty score0.424

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 integrity0.0000.000
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 head0.033
GPT teacher head0.374
Teacher spread0.341 · 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

Citations26
Published2016
Admission routes1
Has abstractyes

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