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Record W2623302193 · doi:10.1097/dss.0000000000001152

Determinants and Timeline of Perioperative Anxiety in Mohs Surgery

2017· article· en· W2623302193 on OpenAlexaff
Irèn Kossintseva, David Zloty

Bibliographic record

VenueDermatologic Surgery · 2017
Typearticle
Languageen
FieldMedicine
TopicNonmelanoma Skin Cancer Studies
Canadian institutionsSKiN HealthUniversity of British Columbia
Fundersnot available
KeywordsAnxietyCosmesisMedicinePerioperativeMohs surgeryVisual analogue scaleCancerSurgeryPsychiatryInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Patients undergoing Mohs micrographic surgery (MMS) exhibit anxiety relating to cancer cure or the expected cosmetic outcome. OBJECTIVE: To obtain quantitative measurements of perioperative cancer and cosmetic anxiety levels in first-time MMS patients. Parameters influencing anxiety and its natural course were assessed. METHODS: Prospective, single-blinded, questionnaire study of 173 patients undergoing MMS of the face. Anxiety levels were assessed using a visual analog scale preoperatively and postoperatively over 6 months. RESULTS: Mohs patients demonstrate a trend to greater or equal anxiety about cancer over cosmesis at all measured time points, but differences only reached statistical significance beginning 1 week postoperatively. Clinically relevant lowering of cancer anxiety levels is delayed until 3 months postoperatively. Cosmetic anxiety reaches a clinically relevant improvement by 1 week. The intuitive predictors of cosmetic anxiety, namely female gender and younger age, were quantitatively reinforced in this study. The predictor of cancer anxiety was the use of preoperative lorazepam. CONCLUSION: To maximize patient care, Mohs surgeons must be aware of covert patient anxieties and the parameters, which influence these anxieties. Identifying and anticipating the course of cancer- and cosmetic-related anxieties will reduce patient fears, improving their satisfaction with the MMS experience.

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 score0.008
Threshold uncertainty score0.559

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.043
GPT teacher head0.314
Teacher spread0.271 · 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

Citations20
Published2017
Admission routes1
Has abstractyes

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