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Optical strategies for diagnosis and treatment of melanoma

2018· dissertation· en· W2782702462 on OpenAlexfundno aff
Layla Pires

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldMedicine
TopicPhotodynamic Therapy Research Studies
Canadian institutionsnot available
FundersCiência sem FronteirasConselho Nacional de Desenvolvimento Científico e TecnológicoFundação de Amparo à Pesquisa do Estado de São PauloPrincess Margaret Cancer Foundation
KeywordsMelanomaMedicinePhotodynamic therapyEnucleationSkin cancerDermatologyRadiation therapyMetastasisCancerSurgeryCancer researchInternal medicine

Abstract

fetched live from OpenAlex

Melanoma is a pigmented tumor that originates from the melanocytes; pigmented cells present throughout the body, including skin and iris. The cutaneous form is the most common type, and it represents about 5% of the skin tumors diagnosed in Brazil. Although it does not have a high incidence, it represents about 80% to 85% of all skin tumor deaths. The second most frequent type of melanoma is ocular. It represents 5% of all melanoma cases and is a potentially lethal disease, especially when it causes metastasis. The main therapeutic approach for melanomas, in general, is surgery, with resection of the cutaneous lesion or enucleation in the case of ocular melanoma. Other techniques such as adjuvant immunotherapy, palliative chemotherapy, and radiotherapy are also used. However, they have low efficacy and several side effects. Photodynamic therapy is a therapeutic modality based on the interaction of light at specific wavelength and photosensitizer, in the presence of molecular oxygen, leading the cell to death. As melanoma is a pigmented cancer, it usually does not respond well to photodynamic therapy due to the high absorption of light on the surface of the tumor, making volumetric eradication impossible. This project investigated optical strategies for the diagnosis and treatment of melanoma.

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)0.000
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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.782
Threshold uncertainty score0.397

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.038
GPT teacher head0.384
Teacher spread0.346 · 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 designOther design
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
Published2018
Admission routes1
Has abstractyes

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