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Record W2410654226

Removal of refractory erosive-atrophic lichen planus by the CO2 laser.

2014· article· en· W2410654226 on OpenAlexaff
Atesa Pakfetrat, Farnaz Falaki, Farzaneh Ahrari, Salma Bidad

Bibliographic record

VenuePubMed · 2014
Typearticle
Languageen
FieldDentistry
TopicOral Health Pathology and Treatment
Canadian institutionsTellabs (Canada)
Fundersnot available
KeywordsMedicineOral lichen planusRefractory (planetary science)LesionSurgeryBurning SensationInternal medicineDermatology
DOInot available

Abstract

fetched live from OpenAlex

UNLABELLED: AIM, STUDY AND BACKGROUND: The erosive-atrophic form of Oral lichen Planus (OLP) is often associated with severe pain and burning sensation. This study investigated the efficacy of CO2 laser surgery for management of refractory erosive-atrophic OLP. METHODS: Ten patients with thirteen erosive-atrophic OLP resistant to standard therapy participated in this study. The size and clinical scores of the lesions and the level of pain/discomfort were recorded before treatment. The lesions were then removed with a CO2 laser device (10600 nm, continuous wave, 5 W, slightly defocused). The subjects were evaluated 1 month and 3 months later and the response rate was assessed according to the decrease in pain, sign scores and size of the lesions. RESULTS: There was a significant reduction in pain and lesion size at 1 and 3 months following laser treatment (p<0.05). The sign scores of the lesions were also significantly improved at follow-up periods compared to the pretreatment state (p<0.05). At the end of the follow-up period, 54% of the lesions showed 3 or 4 degrees of improvement in the clinical score and 23% improved 1 or 2 degrees, whereas 23% remained unchanged post-operatively compared to the pretreatment evaluation. CONCLUSION: The present results indicate that the CO2 laser surgery is an effective modality for management of erosive-atrophic OLP and can be considered as a suitable alternative to standard 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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.752
Threshold uncertainty score0.300

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.024
GPT teacher head0.257
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

Citations14
Published2014
Admission routes1
Has abstractyes

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