DOI:http://dx.doi.org/10.7314/APJCP.2013.14.4.2155 Global Controversies and Advances in Skin Cancer- Brisbane 2013
Bibliographic record
Abstract
Skin cancer is broadly classified by two key types, melanoma and non-melanoma skin cancer. The latter are not routinely collected by cancer registration as many are treated in doctor’s surgeries using destructive techniques that preclude histological confirmation (Cancer Council Queensland, 2013a). Melanoma of the skin is not an insignificant problem worldwide. In 2008 in the South-East Asian Region (SEARO) as classified by the World Health Organisation (WHO) there was an estimated incidence of 2800 cases of melanoma of the skin in 2008 and a staggering 20782 cases in the Western Pacific Region (WPRO) (IARC, 2008). Of note, Australia and New Zealand are included in the WPRO classification and melanoma incidence rates in Australia and New Zealand are two to three times as high as those found in Canada, the United States and the United Kingdom. Although mortality rates are quite low, they are still approximately two times higher in Australia and New Zealand than in Canada, the United States and the United Kingdom (IARC 2008). The incidence of both non-melanoma and melanoma skin cancers has been increasing over the past decades with between 2 and 3 million non-melanoma skin cancers and 132,000 melanoma skin cancers occur globally each year (WHO, 2013). Skin Cancer- a Costly Disease In a report to identify the burden and cost of non-melanoma skin cancer (NMSC) treatments in Australia and to project estimates of numbers and costs to 2015, Fransen et al (2012) noted the total number of NMSC treatments increased from 412 493 in 1997 to 767 347
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.362 | 0.184 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".