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Record W2966606678 · doi:10.1111/ajr.12545

Epidemiology of melanoma in rural southern Queensland

2019· article· en· W2966606678 on OpenAlexaff
Scott Kitchener, Janani Pinidiyapathirage, Keegan Hunter, Lynsey Cochrane, Stephanie Gederts, Toshev SY, Brianna Watts, Adrienne Murray, Manish Poologasundrum, Swaha Bose, John Hall, Andrew Reedy, Lynton Alfred Hudson, Matthew Masel

Bibliographic record

VenueAustralian Journal of Rural Health · 2019
Typearticle
Languageen
FieldMedicine
TopicCutaneous Melanoma Detection and Management
Canadian institutionsMiller Group (Canada)
Fundersnot available
KeywordsMedicineEpidemiologyMelanomaAuditRural areaRural communityPrimary careMetropolitan areaCancer registryFamily medicineDemographyPathology

Abstract

fetched live from OpenAlex

OBJECTIVE: The objective of this study is to define the epidemiology of melanoma in rural communities in southern Queensland. DESIGN: The design used was a 6-year clinical record audit of melanoma cases identified by billing records and electronic clinical records, confirmed and typed with histology. SETTING AND PARTICIPANTS: This study was based on seven agricultural communities on the Darling Downs with patients presenting to local primary care clinics. MAIN OUTCOME MEASURES: Outcomes measured were confirmed type, depth and anatomic distribution of melanoma identified at these practices during the study period. RESULTS: = 9.6, P < 0.05) to that reported previously from the Queensland Cancer Registry. A high proportion (87%) of melanoma diagnosed by these general practitioners were 1 mm or less when treated. CONCLUSIONS: Conclusions drawn from these findings are that melanoma risk is not so much lesser in rural, inland communities compared with coastal and metropolitan regions, but different. Differences may relate to comprehensive data capture available in rural community studies and to different sun exposure and protection behaviours. The higher proportion of melanoma identified at early stages suggests rural primary care is an effective method of secondary prevention.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.026
Threshold uncertainty score0.486

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0000.000

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 head0.034
GPT teacher head0.332
Teacher spread0.298 · 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 designObservational
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".

Quick stats

Citations2
Published2019
Admission routes1
Has abstractyes

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