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Record W3204720412 · doi:10.30683/1929-2279.2019.08.08

Do "Incidental Melanomas" Exist? If so, how many are they? High Time to Decide

2019· article· en· W3204720412 on OpenAlexvenueno aff
Giovanni Luigi Capella

Bibliographic record

VenueJournal of cancer research updates · 2019
Typearticle
Languageen
FieldMedicine
TopicCutaneous Melanoma Detection and Management
Canadian institutionsnot available
Fundersnot available
KeywordsMelanomaMedicineDiseaseLesionDermatologyTime of deathPathologyCancer researchMedical emergency

Abstract

fetched live from OpenAlex

Do "Clinically Insignificant, HIstological MElanoma-Like Lesions" (CIHIMELL) - to wit, merely incidental, looking-like-melanoma lesions devoid of intrinsic malignant potential - exist? The question arises from the fact that, in spite of increased diagnoses of completely excised malignant melanoma (MM) in the last two decades, mortality from advanced metastatic disease has not decreased. After a brief review of the literature, the author proposes that the existence of CIHIMELL could be affirmed through post mortem dermoscopy and histological study of any pigmented lesion, clinically or dermoscopically suspected, of several deceased patients undergoing necropsy for death causes unrelated to MM. Should the cumulative prevalence of merely histological melanomas turn out to be exceedingly high and not commensurable with the current death rates of true MM, the discrepancy would tangibly prove that indolent pigmented lesions with morphological aspect of melanoma do exist. This would clearly introduce difficulties that could satisfactorily be dealt with only through a paradigm shift in melanoma surveillance and diagnosis (which could also allow to add a piece of encouraging uncertainty to the patient-physician relationship). However, it can be expected that such an enterprise would be countered by several vested academic and commercial interests.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.236
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.002

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 head0.026
GPT teacher head0.348
Teacher spread0.323 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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

Citations1
Published2019
Admission routes1
Has abstractyes

Explore more

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