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Record W4416390917 · doi:10.1016/j.pdpdt.2025.104960

Effective treatment depth of photodynamic therapy after partial debulking of nodular basal cell carcinoma

2025· article· en· W4416390917 on OpenAlexaff
Ana Gabriela Sálvio, Mirian Denise Stringasci, Vanderlei Salvador Bagnato, Cristina Kurachi, Layla Pires, Brian C. Wilson

Bibliographic record

VenuePhotodiagnosis and Photodynamic Therapy · 2025
Typearticle
Languageen
FieldMedicine
TopicNonmelanoma Skin Cancer Studies
Canadian institutionsPrincess Margaret Cancer Centre
Fundersnot available
KeywordsDebulkingPhotodynamic therapyLesionBasal cell carcinomaBiopsyUltrasound

Abstract

fetched live from OpenAlex

Significance Tumor debulking before topical photodynamic therapy PDT increases the efficiency, however, this raises question of how much of the residual lesion is actually treated. Approach BCC thickness was measured before and after debulking by ultrasound and related to PDT effectiveness. The debulked material was histologically evaluated, and 30 days after treatment, a 2 mm punch biopsy were performed. Results The thickness measured by ultrasound before and after debulking ranged from 0.9 to 2.3 mm(mean 1.8±0.4mm) and from 0.5 to 1.9 mm(mean 1.3±0.3mm), respectively. This represents a 30%reduction in lesion thickness(0.6±0.3mm average debulking depth)(p<0.001). Clearance rate was 86%, however, lesions less than 1.4mm thick after debulking had 100% clearance. Conclusions Obtaining the precise thickness of BCC using ultrasound imaging could allow successful PDT treatment of thicker BCC lesions as long as the post-debulking thickness is <2 mm, with complete clearance being achievable at <1.4 mm, suggesting that the use of ultrasound imaging is a valuable adjunct for the precise use of PDT in nodular BCC.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.263
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
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 head0.009
GPT teacher head0.268
Teacher spread0.258 · 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.

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

Citations0
Published2025
Admission routes1
Has abstractyes

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