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Record W3034088199 · doi:10.1080/09546634.2020.1781757

The application of medical scale in the treatment of plantar warts: analysis and prospect

2020· review· en· W3034088199 on OpenAlexaff
Kai Huang, Mingjia Li, Yi Xiao, Lisha Wu, Yixin Li, Yang Yang, Guanzhong Shi, Nianzhou Yu, Dihui Liu, Juan Su, Xianggui Wang, Shuang Zhao, Xiang Chen

Bibliographic record

VenueJournal of Dermatological Treatment · 2020
Typereview
Languageen
FieldMedicine
TopicGenital Health and Disease
Canadian institutionsSKiN Health
Fundersnot available
KeywordsPlantar wartsMedicineCryotherapyAdverse effectConjoint analysisQuality of life (healthcare)Physical therapyDermatologySurgeryInternal medicine

Abstract

fetched live from OpenAlex

Plantar warts are common cutaneous diseases on the sole caused by the human papillomavirus, with a high annual incidence rate of 14%. It often causes pain, which impairs quality of life of patients. Numerous therapeutic options for plantar warts exist with variable success. However, all of them, including first-line treatment, have different adverse reactions or high recurrence rates. There is no one effective method for all patients. The choice of treatment method puzzles doctors. With the help of medical scales, we can analyze the patients' condition, so as to guide the choice of treatment methods, which is of great significance for the individualized treatment of patients with plantar warts. This review takes cryotherapy, intralesional injection of bleomycin and photodynamic therapy as examples to discuss the application of medical scales in the treatment of plantar warts, summarizes the scales that can be used to evaluate the status of plantar wart, adverse reactions, prognosis and patient's financial situation, and discusses their clinical and scientific value. We hope to use scales to consider the severity of plantar warts and economic level, help different patients to choose different treatment options, and make suggestions on the evaluation of the adverse reactions and treatment effect.

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 score0.968
Threshold uncertainty score0.314

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
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 head0.051
GPT teacher head0.398
Teacher spread0.347 · 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

Citations13
Published2020
Admission routes1
Has abstractyes

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