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Record W4394158678 · doi:10.6084/m9.figshare.20581014

Additional file 1 of A two-stage genome-wide association study to identify novel genetic loci associated with acute radiotherapy toxicity in nasopharyngeal carcinoma

2022· dataset· en· W4394158678 on OpenAlexaff
Yang Wang, Fan Xiao, Yi Zhao, Chen‐Xue Mao, Lu-Lu Yu, Lei‐Yun Wang, Qi Xiao, Rong Liu, Xi Li, Howard L. McLeod, Biwen Hu, Yu‐Ling Huang, Qiao‐Li Lv, Xiaoxue Xie, Weihua Huang, Wei Zhang, Chengxian Guo, Jingao Li, Ji‐Ye Yin

Bibliographic record

VenueFigshare · 2022
Typedataset
Languageen
FieldMedicine
TopicHead and Neck Cancer Studies
Canadian institutionsBruyèreCentre for Advancing Health OutcomesUniversity of British Columbia
Fundersnot available
KeywordsNasopharyngeal carcinomaBiologyRadiation therapyStage (stratigraphy)GeneticsOncologyMedicineInternal medicine

Abstract

fetched live from OpenAlex

Additional file 1: Fig. S1. Diagram of data processing flow. Bioinformatics tools utilized in each step were showed in blue in the brackets. Detailed parameters and quality control criteria were indicated with red. Fig. S2. Distribution of samples according to PCA analysis in discovery stage. The red and green spots represented two different groups of patients. The results showed that no stray samples appeared in all five toxicities. Fig. S3. Quantile–quantile (QQ) plot of observed association P values (y-axis) against expected P values (x-axis) in the discovery stage. Fig. S4. Establishment of prediction models for skin reaction (A and B) and dysphagia toxicities (C and D). For each toxicity, patients were firstly randomly divided into two groups, which used to establish (A and C) and test models (B and D) respectively. Then, three multivariable logistic regression models with genetic factors only, clinical factors only and combination of both genetic and clinical factors were established. The genetic model only involved genetic factors: rs6711678, rs4848597, rs4848598 and rs2091255 for skin reaction, and rs584547 for dysphagia. During the calculation, rs6711678, rs4848597, rs4848598 and rs2091255 were combined as polygenic risk scores. The clinical model involved clinical factors only, which include age, sex, BMI, smoking status, stage, EBV infection and radiotherapeutic regimen. The combined model integrated both genetic and clinical factors. BMI: body mass index, EBV: Epstein-Barr virus, AUC: area under curve. Fig. S5. The MAF of rs6711678, rs4848597, rs4848598, rs2091255 and rs584547 in different ethnic populations. AFR: African, EAS: East Asian, EUR: Europe, AMR: American, SAS: South Asian, LAM: Latin American. Table S1. Characteristics of NPC patients involved in skin reaction association analysis. Table S2. Characteristics of NPC patients involved in dysphagia association analysis. Table S3. Characteristics of NPC patients involved in oral mucositis association analysis. Table S4. Characteristics of NPC patients involved in salivary glands toxicity association analysis. Table S5. Characteristics of NPC patients involved in myelosuppression association analysis. Table S6. Stratified analysis of the association between skin reaction and chromosome 2q14.2 loci. Table S7. Association between therapeutic response and chromosome 2q14.2 loci in the stratified patients.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.026
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.795
Threshold uncertainty score0.292

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.026
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0020.003
Science and technology studies0.0010.000
Scholarly communication0.0020.002
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.7950.057

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 head0.311
Teacher spread0.279 · 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 source (direct Gemma or distilled Codex), 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
Published2022
Admission routes1
Has abstractyes

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