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Record W2415883367 · doi:10.1097/dss.0000000000000546

Evaluation of the Merz Hand Grading Scale After Calcium Hydroxylapatite Hand Treatment

2015· article· en· W2415883367 on OpenAlexaff
Vince Bertucci, Nowell Solish, Martin C. S. Wong, Michael Howell

Bibliographic record

VenueDermatologic Surgery · 2015
Typearticle
Languageen
FieldMedicine
TopicFacial Rejuvenation and Surgery Techniques
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMedicineGrading scaleDentistryDorsumGrading (engineering)Randomized controlled trialSurgery

Abstract

fetched live from OpenAlex

BACKGROUND: Measurement scales that quickly and rigorously evaluate the effectiveness of filler treatment in hands are important tools in clinical practice. The Merz Hand Grading Scale (MHGS) is used to grade the appearance of the dorsal hand. The MHGS has been validated for photographic and live assessment of the hands. OBJECTIVE: To evaluate the sensitivity of the 5-point MHGS to detect clinically meaningful and aesthetically pleasing changes in hand appearance after treatment with a calcium hydroxylapatite (CaHA)-based dermal filler. METHODS: The controlled 4-week study randomized 30 subjects (60 hands) 2:1 to a Treatment group (treatment at enrollment) or a Control group (treatment at end of study). Effectiveness was evaluated with live MHGS ratings and photographic assessments with the Global Aesthetic Improvement Scale (GAIS). RESULTS: At Week 4, all Treatment group subjects (20/20) achieved a ≥1-point improvement on the MHGS compared with 0/10 (0%) of the Control group (p < .0001). Subjects and treating physicians rated 92.5% (37/40) and 100% (40/40), respectively, of hands as at least "improved," using the GAIS. CONCLUSION: The MHGS is an appropriate and validated tool that clinicians can use to counsel patients and evaluate clinically meaningful and aesthetically pleasing changes after hand treatment with CaHA.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.706
Threshold uncertainty score0.332

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.136
GPT teacher head0.337
Teacher spread0.202 · 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

Citations27
Published2015
Admission routes1
Has abstractyes

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