MétaCan
Menu
Back to cohort
Record W4410767710 · doi:10.1071/pu24008

Beyond the blind spot: considering the benefits of comprehensive skin cancer surveillance

2025· article· en· W4410767710 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenuePublic Health Research & Practice · 2025
Typearticle
Languageen
FieldMedicine
TopicNonmelanoma Skin Cancer Studies
Canadian institutionsWorkplace Health, Safety and Compensation Commission
Fundersnot available
KeywordsBlind spotMedicineComputer scienceEnvironmental healthArtificial intelligence

Abstract

fetched live from OpenAlex

Australia has the world's highest skin cancer rates. The keratinocyte cancers (basal cell carcinoma [BCC] and squamous cell carcinoma [SCC]) are the most common and costly, yet unlike melanoma, they are not nationally registered, and the lack of registry data hinders control efforts. The Tasmanian cancer registry collects data on BCC and SCC incidence, revealing concerning trends and high-risk groups. International examples show how registry data inform policy and prevention. Comprehensive registration would enable similar benefits for Australia. We propose a phased approach, starting with high-risk lesions, alongside standardised pathology reporting and the potential use of artificial intelligence, and recommend an evaluation of the cost of this integrated strategy.

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.

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)0.011
metaresearch head score (Gemma)0.011
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.856
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0110.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.002
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 head0.276
GPT teacher head0.500
Teacher spread0.224 · 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