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'Optimum mobility' facelift. Part 2 – the technique

2006· article· en· W4231446672 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenuePlastic Surgery · 2006
Typearticle
Languageen
FieldEngineering
TopicAssembly Line Balancing Optimization
Canadian institutionsMcGill University
Fundersnot available
KeywordsComputer scienceBusiness

Abstract

fetched live from OpenAlex

In the first of this two-part article on the 'optimum mobility' facelift, facial tissue mobility was analyzed, and three theories or mechanisms emerged: 'intrinsic mobility', 'surgically induced mobility' and 'optimum mobility points'. In this second part, these three theories are applied to a rhytidectomy procedure termed 'optimum mobility' facelift. Before surgery, 'optimum mobility points' are marked on the skin. During surgery, the subcutaneous dissection is kept to a minimum by carrying it out precisely to these 'optimum mobility points'. The facial tissues, with their skin and superficial musculoaponeurotic system attachments intact, are then mobilized laterally using the 'intrinsic mobility' phenomenon, and this mobilization fixed in place using mattress sutures. The 'optimum mobility' facelift is an efficient rhytidectomy technique that has a thoughtful, precise plan, a low complication rate, a fast recovery and very satisfactory results.

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.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.579
Threshold uncertainty score0.461

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 integrity0.0000.000
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 head0.008
GPT teacher head0.183
Teacher spread0.175 · 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