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Additional file 3 of Development of a 3D functional assay and identification of biomarkers, predictive for response of high-grade serous ovarian cancer (HGSOC) patients to poly-ADP ribose polymerase inhibitors (PARPis): targeted therapy

2020· other· en· W6958252366 on OpenAlexaff

Bibliographic record

VenueFigshare · 2020
Typeother
Languageen
FieldSocial Sciences
TopicLegal and Regulatory Analysis
Canadian institutionsUniversité LavalHôtel-Dieu de Québec
Fundersnot available
KeywordsOlaparibOvarian cancerSerous fluidSerous ovarian cancerSynthetic lethalityIdentification (biology)

Abstract

fetched live from OpenAlex

Additional file 3. Comparative analysis of PARPis-sensitive and resistant AsPCs, as examined in monolayer vs. 3D culture. (A) Total number of AsPCs determined as resistant or sensitive to treatment with the two PARPis olaparib and niraparib when grown in monolayer. Olaparib resistant AsPCs showed to be 36% higher in total number to niraparib resistant AsPCs, likewise niraparib sensitive AsPCs showed to be 36% higher in total number to olaparib sensitive AsPCs. (B) Total number of AsPCs determined as resistant or sensitive to treatment with the two PARPis olaparib and niraparib when treated in 3D. Olaparib resistant AsPCs showed to be 28% higher in total number to niraparib resistant AsPCs, likewise niraparib sensitive AsPCs showed to be 28% higher in total number to olaparib sensitive AsPCs.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.695
Threshold uncertainty score0.614

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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)0.6950.000

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 head0.019
GPT teacher head0.250
Teacher spread0.231 · 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.

Study designNot applicable
Domainnot available
GenreDataset

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

Citations0
Published2020
Admission routes1
Has abstractyes

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