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Record W6976992537 · doi:10.6084/m9.figshare.17066221

Additional file 2 of Risk of non-melanoma skin cancer with biological therapy in common inflammatory diseases: a systemic review and meta-analysis

2021· article· en· W6976992537 on OpenAlexaboutno aff

Bibliographic record

VenueFigshare · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicEvasion and Academic Success Factors
Canadian institutionsnot available
Fundersnot available
KeywordsSkin cancerBiological response modifiersCancerMeta-analysisBasal cell carcinomaRelative riskHuman skinConfidence interval

Abstract

fetched live from OpenAlex

Additional file 2: Fig. S1. Forest plot for the subgroup analysis of the risk for non-melanoma skin cancer (NMSC) in patients with common inflammatory diseases receiving biologics compared with patients not receiving biologics classified by (a) types of NMSC, (b) types of biological treatments, (c) treatment years, (d) Newcastle–Ottawa Quality Assessment Scale (NOS) score, and (e) sample size. RA: rheumatoid arthritis; IBD: inflammatory bowel disease; TNFI: tumor necrosis factor inhibitor; BCC: basal cell skin cancer; SCC: squamous cell skin cancer; RR, relative risk; CI, confidence interval. The shadow boxes represent point estimates, and the horizontal lines represent 95% CIs. The weight of the research is reflected by the size of the box. Diamonds represent pooled estimates, with their tips representing 95% CIs

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)0.005
metaresearch head score (Gemma)0.058
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: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.784
Threshold uncertainty score0.309

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.058
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.005
Bibliometrics0.0050.008
Science and technology studies0.0010.000
Scholarly communication0.0020.003
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.7840.035

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 head0.067
GPT teacher head0.333
Teacher spread0.266 · 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 designMeta-analysis
Domainnot available
GenreReview

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
Published2021
Admission routes1
Has abstractyes

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