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Expression of p63 in Squamous Cell Carcinoma of the Lung and its Diagnostic Significance: A Meta-Analysis

2012· article· en· W2119267711 on OpenAlexvenueno aff
Bibo Wang, Yiping Han, Jiajie Zang

Bibliographic record

VenueJournal of cancer research updates · 2012
Typearticle
Languageen
FieldMedicine
TopicCancer-related Molecular Pathways
Canadian institutionsnot available
Fundersnot available
KeywordsInternal medicineGastroenterologyBasal cellMedicineMeta-analysisLung cancerCarcinomaPathology

Abstract

fetched live from OpenAlex

Introduction: The expression of p63 has been studied in various tumor types, including squamous cell carcinoma (SCC). Methods and Results: Twenty-five trials met the inclusion criteria with a total of 1,193 patients. The overall positive proportion of p63 was 91.5% (95% CI, 86.3-94.8). Both histological and cytological methods of obtaining specimens showed a high expression of p63 in SCC at 89.8% (95% CI, 81.9-94.5) and 88.7% (95% CI, 80.9-93.6). The p63 positive proportion of the well or moderately differentiated subgroups was 92.7% (95% CI, 77.9-97.9) compared to the poorly differentiated subgroup at 86.9% (95% CI, 61.6-96.5). When using >1% of p63 immunoreactive cells as the positive standard, both sensitivity and specificity at 0.91 (95% CI, 0.86-0.94) and 0.80 (95% CI, 0.75-0.85), respectively, were acceptable. When using >10% and >50% standards, sensitivities of 0.92 (95% CI, 0.90-0.94) and 0.82 (95% CI, 0.78-0.85) and specificities of 0.84 (95% CI, 0.82-0.86) and 0.92(95% CI, 0.90-0.94) were shown. Conclusions: In SCC, there is a high expression of p63, which has no association with the histological or cytological methods used to obtain specimens or the degree of differentiation of the specimens. Even when only a small amount of cells were stained (>1%) as the positive standard, the sensitivity and specificity of p63 were maintained at a high level. We suggest that >50% of immunoreactive cells be used as the positive standard to achieve proper sensitivity and specificity.

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)0.002
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.157
Threshold uncertainty score0.429

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.077
GPT teacher head0.383
Teacher spread0.306 · 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".

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Citations0
Published2012
Admission routes1
Has abstractyes

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