MétaCan
Menu
Back to cohort
Record W3035975652 · doi:10.1002/lary.28824

<scp>In‐Office KTP</scp> Laser for Treating Hereditary Hemorrhagic Telangiectasia–Associated Epistaxis

2020· article· en· W3035975652 on OpenAlexaff
Vincent Wu, Erika Kell, Marie E. Faughnan, John M. Lee

Bibliographic record

VenueThe Laryngoscope · 2020
Typearticle
Languageen
FieldMedicine
TopicVascular Anomalies and Treatments
Canadian institutionsUniversity of TorontoSt. Michael's Hospital
Fundersnot available
KeywordsTelangiectasiaMedicineDermatology

Abstract

fetched live from OpenAlex

OBJECTIVE: To evaluated the efficacy and safety of in-office potassium titanyl phosphate (KTP) laser treatment for the management of epistaxis in hereditary hemorrhagic telangiectasia (HHT) patients. METHODS: A retrospective case series of all HHT patients over age of 18 who underwent in-office KTP laser treatment from July 1, 2017 to December 31, 2019 was performed. The primary outcome measure was the epistaxis severity score (ESS) pre- and post-procedure. Secondary outcome measures included patient reported pain (on a 10-point Likert-type scale), and procedural adverse events and complications. RESULTS: A total of 16 patients underwent KTP in-office laser treatment during the review period. There was both a clinically and statistically significant decrease in the ESS after in-office laser treatment, baseline ESS -7.24, SD 1.71, follow up ESS -4.92, SD 1.83 (mean difference 2.94, 95% confidence interval, 1.83-4.04, P < .0001). There were no reported adverse events or complications associated with the procedure. The mean pain score reported was 0.19, SD 0.75. The average blood loss was 10.8 mL, SD 37.3. The majority of patients (62.5%, 10/16) had no blood loss during the procedure. CONCLUSION: Clinically and statistically significant decreases were noted in the ESS of HHT patients after in-office KTP laser photocoagulation. The procedure was well tolerated by patients, without any adverse events or complications. LEVEL OF EVIDENCE: 4 Laryngoscope, 131:E689-E693, 2021.

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)0.001
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 score0.079
Threshold uncertainty score0.658

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.020
GPT teacher head0.253
Teacher spread0.233 · 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

Citations5
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueThe LaryngoscopeSame topicVascular Anomalies and TreatmentsFrench-language works237,207