MétaCan
Menu
Back to cohort
Record W2892142751 · doi:10.1111/jocd.12761

Histological findings correlated with clinical outcomes in telangiectasia treated with ohmic thermolysis and 940 nm laser

2018· article· en· W2892142751 on OpenAlexaff
Ronald G. Bush, Peggy Bush

Bibliographic record

VenueJournal of Cosmetic Dermatology · 2018
Typearticle
Languageen
FieldMedicine
TopicDermatologic Treatments and Research
Canadian institutionsSt. Jerome's University
Fundersnot available
KeywordsSclerotherapyReticular DermisTelangiectasiaMedicineReticular connective tissueDermisThrombusPapillary dermisEpitheliumSurgeryPathology

Abstract

fetched live from OpenAlex

BACKGROUND: Heat modalities are commonly used as either primary or adjunctive treatment for telangiectasia. Minimal information is available as to the nature of injury to the vessel and surrounding tissue. METHOD: A total of 135 patients were treated over a 2-year period using ohmic thermolysis (45), 940 nm laser (50), and 940 nm laser with sclerotherapy (40). After treatment, 1 mm biopsies were done in selected patients in each group. Clinical correlation was studied in each group by observing vessel response at 4-6 weeks postprocedure. RESULTS: Ohmic thermolysis produces electrodessication of the squamous epithelium, reticular dermis, and fusion of the target vessel. 940 nm laser results include squamous epithelial damage, subcutaneous water blister, collagen denaturation, and vessel endothelial cell loss with thrombus at point of maximal impact. The addition of sclerotherapy at time of laser potentiates vessel damage. There was no long-term skin sequelae after treatment when each device is used at recommended settings and on appropriate vessel size. CONCLUSION: Each device causes damage to the squamous epithelium and papillary reticular dermis that is transient. Ohmic thermolysis provides vessel clearance of >90% in telangiectasias <0.5 mm. 940 nm laser effectiveness is <70% for vessel clearance, but improves to >90% when sclerotherapy is performed at time of treatment.

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 score0.066
Threshold uncertainty score0.431

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.027
GPT teacher head0.342
Teacher spread0.314 · 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
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Cosmetic DermatologySame topicDermatologic Treatments and ResearchFrench-language works237,207