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Record W4281294257 · doi:10.1080/09546634.2022.2067814

Preliminary efficacy and safety analysis: 12-month results in 83 patients using a novel approach of widefield radiation therapy for extensive skin field cancerization with or without keratinocyte cancers

2022· article· en· W4281294257 on OpenAlexaff
Andrew E. Potter, Christopher M. Baker, Stephen Shumack, Robert Sinclair, Walter J. Curran, David Christie, Bradley Wong, Peter Foley, Peter C. O’Brien, Lynda Spelman

Bibliographic record

VenueJournal of Dermatological Treatment · 2022
Typearticle
Languageen
FieldMedicine
TopicNonmelanoma Skin Cancer Studies
Canadian institutionsProbity Medical Research
Fundersnot available
KeywordsMedicineCosmesisField cancerizationDermatologySkin cancerRadiation therapyClinical trialCancerSurgeryInternal medicine

Abstract

fetched live from OpenAlex

PURPOSE: Evaluate the use of widefield radiation therapy (RT) in the management of extensive skin field cancerization (ESFC) with/without keratinocyte cancer (KC). METHODS: The National Dermatology Radiation Oncology Registry is a multidisciplinary collaboration (dermatologists and radiation oncologists). It captures disease description, prior therapies, radiation prescription, clinical effect, skin cosmesis scores, and toxicity data. This analysis included 12-month follow-up data on 89 treated fields from a subset of 83 patients. RESULTS: = 25) ESFC with KC. Recurrence (4/89 [5%]) and appearance of new lesions (10/89 [11%]) were minimal. Cosmetic outcome was excellent/good in 98% ESFC and 96% ESFC with KC. Grade 1-2 acute radiation dermatitis occurred in up to 80% of treated fields. The frequency of Grade 3 acute skin toxicities was low. CONCLUSIONS: Registry data demonstrate the potential for widefield RT to treat patients with significant skin pathology who have exhausted other therapies and require durable, minimally invasive treatment options. At 12 months, observed clinical success rates were higher than those reported for topical interventions for ESFC. Ongoing follow-up is required to determine longer term outcomes.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.092
Threshold uncertainty score0.371

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.044
GPT teacher head0.310
Teacher spread0.266 · 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

Citations4
Published2022
Admission routes1
Has abstractyes

Explore more

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