MétaCan
Menu
Back to cohort
Record W2180436174 · doi:10.1111/jocd.12188

Ergonomics in hair restoration surgeons

2015· article· en· W2180436174 on OpenAlexaff
Ken L. Williams, Aditya Gupta, Hayden Schultz

Bibliographic record

VenueJournal of Cosmetic Dermatology · 2015
Typearticle
Languageen
FieldHealth Professions
TopicOccupational health in dentistry
Canadian institutionsMediprobe Research (Canada)University of Toronto
Fundersnot available
KeywordsMedicineHuman factors and ergonomicsQuality of life (healthcare)Physical therapySurgeryPoison controlMedical emergencyNursing

Abstract

fetched live from OpenAlex

BACKGROUND: Musculoskeletal disorders (MSDs) are potential sources of morbidity in hair restoration surgeons (HRS). This is particularly true for those who perform follicular unit extraction (FUE). OBJECTIVE: To describe the nature, prevalence, and extent of ergonomic or work-related MSDs among HRS. METHODS & MATERIALS: A survey regarding MSDs was e-mailed to 100 HRS. RESULTS: Thirty-eight HRS completed the survey, the majority of which were male and between the ages of 50-69. Fifty percent of respondents reported musculoskeletal symptoms occurring during or after hair restoration procedures. Reports of pain during and after surgery were higher for FUE procedures than single strip excision procedures. Pain/fatigue/discomfort persisted for longer following FUE procedures compared to strip excision procedures. MSD symptoms also negatively impacted quality of life. Although the majority of respondents felt that ergonomics was important, only 30% use ergonomic support when performing FUE procedures. CONCLUSION: Hair restoration surgeons should be aware of MSD symptoms and particularly when performing FUE. Symptoms reported included pain, fatigue, and discomfort, sometimes lasting several hours following surgery. More attention needs to be paid to ergonomics during hair restoration procedures in order to improve the quality of life of surgeons and ultimately prevent the development of MSDs.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.031
Threshold uncertainty score0.367

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 integrity0.0000.001
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 head0.135
GPT teacher head0.468
Teacher spread0.334 · 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
Published2015
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Cosmetic DermatologySame topicOccupational health in dentistryFrench-language works237,207