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Local Anesthesia in Hair Transplantation

2002· review· en· W1994011203 on OpenAlexaff
David Seager, Cam Simmons

Bibliographic record

VenueDermatologic Surgery · 2002
Typereview
Languageen
FieldMedicine
TopicAnesthesia and Pain Management
Canadian institutionsThe Scarborough Hospital
Fundersnot available
KeywordsMedicineTransplantationDoseToxicityLocal anesthesiaAnesthesiaIntensive care medicineMEDLINESurgeryPharmacologyInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Safe and effective use of local anesthesia is essential in hair transplantation. OBJECTIVE: To review the agents and techniques of local anesthesia as applied to hair transplantation. METHODS: Information was retrieved from texts, journal articles found by MEDLINE searches, and related references. RESULTS: Agents, vasoconstrictors, maximum dosages, toxicity, and techniques are discussed. CONCLUSION: Effective local anesthesia can be maintained throughout hair transplantation. Care must be exercised to minimize discomfort through proper technique and to minimize toxicity through judicious use of vasoconstrictors and nerve blocks and by monitoring total dosage. Constant monitoring for toxicity is required, as is early intervention in the unlikely event that warning signs should appear.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.951
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.300
Teacher spread0.227 · 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.

Study designNot applicable
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

Citations27
Published2002
Admission routes1
Has abstractyes

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