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Record W1990718043 · doi:10.1080/17429590701523752

Patient‐reported outcomes with botulinum neurotoxin type A

2007· review· en· W1990718043 on OpenAlexaff
Alastair Carruthers, Jean Carruthers

Bibliographic record

VenueJournal of Cosmetic and Laser Therapy · 2007
Typereview
Languageen
FieldMedicine
TopicBotulinum Toxin and Related Neurological Disorders
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMedicineGlabellaFacial rejuvenationPatient satisfactionClinical trialPatient-reported outcomeBotulinum neurotoxinPhysical therapyQuality of life (healthcare)SurgeryForeheadInternal medicineNursing

Abstract

fetched live from OpenAlex

Clinical trials establishing the efficacy of botulinum neurotoxin type A historically have included as outcomes investigator assessments and general patient global assessments of treatment. However, these outcomes are of limited value in determining the specific benefits desired by patients who seek facial rejuvenation. To address this issue, several patient-reported outcomes measures have been developed and utilized in clinical trials of botulinum neurotoxin type A (specifically, the BOTOX Cosmetic brand). The outcomes include the Self-Perception of Age (SPA) measure and the Facial Line Outcomes (FLO) questionnaire. On the FLO questionnaire, patients rate the degree to which their facial lines bother them; make them look older than they would like; prevent them from having a smooth facial appearance; and make them look tired, stressed, or angry when that is not how they feel. Several clinical trials have demonstrated significant improvements in these outcomes from baseline and versus placebo for the treatment of multiple upper facial lines as well as for treating the glabella as a single region. These outcomes data help in understanding patient objectives and motivations, establishing a treatment plan, and ensuring patient satisfaction. Clinicians can use the SPA measure for all patients to help show the value 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: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.992
Threshold uncertainty score0.764

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.073
GPT teacher head0.353
Teacher spread0.280 · 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 designOther design
Domainnot available
GenreReview

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

Citations20
Published2007
Admission routes1
Has abstractyes

Explore more

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