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Record W2077821603 · doi:10.1177/1534734607308249

A Critical Analysis of Measurements Used to Assess and Manage Scars

2007· article· en· W2077821603 on OpenAlex
Claude Roques, Luc Téot

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.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueThe International Journal of Lower Extremity Wounds · 2007
Typearticle
Languageen
FieldMedicine
TopicDermatologic Treatments and Research
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineScarsScale (ratio)Medical physicsPhysical medicine and rehabilitationArtificial intelligenceSurgeryComputer science

Abstract

fetched live from OpenAlex

Scars evolve through a maturation stage during which it is necessary to adapt different treatments. To adapt treatments, it is necessary to assess various parameters linked to inflammation. To this end, clinical scar assessments are subjective though reliable, and validation is operator dependent. The Vancouver Scar Scale, Visual Analogic Scale, Patient and Observer Scar Assessment Scale, and the Manchester Scale assess different scar characteristics. These scales are interesting, depending on the type of scars, and are easy to use but subject to errors. To use clinical a scale, the raters must be trained. Parameters can also be precisely assessed by technical means, whereby they rate only one parameter, but are more accurate. Some scales are easy to use, have low cost, and can be used for clinical assessment. Others scales are more complex and expensive, and can be used in research or treatment evaluation.

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)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.053
Threshold uncertainty score0.185

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0000.000

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 head0.143
GPT teacher head0.433
Teacher spread0.290 · 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