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Record W2507744150 · doi:10.1089/wound.2016.0702

Can Imaging Put the “ <i>Advanced”</i> Back in Advanced Wound Care?

2016· editorial· en· W2507744150 on OpenAlexaff
Ralph S. DaCosta, Kathryn Ottolino‐Perry, Jaideep Banerjee

Bibliographic record

VenueAdvances in Wound Care · 2016
Typeeditorial
Languageen
FieldMedicine
TopicWound Healing and Treatments
Canadian institutionsPrincess Margaret Cancer CentreOntario Council of University LibrariesUniversity of TorontoUniversity Health Network
Fundersnot available
KeywordsWound careMedicineMedical physicsPatient careIntensive care medicineMedical imagingMedical careMedical emergencyRadiologyEmergency medicineNursing

Abstract

fetched live from OpenAlex

in wound diagnostics. Whereas medical imaging is a mainstay in the diagnostic toolkit across many other medical fields (oncology, neurology, gastroenterology, orthopedics, etc.), the field of wound care has yet to realize the full potential that advances in imaging technologies have to offer the clinician. In this issue, the first of a series in wound imaging and diagnostics, four articles have been assembled, highlighting some of the recent advances in wound imaging technologies.

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: Editorial · Consensus signal: Editorial
Teacher disagreement score0.799
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.002
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 head0.005
GPT teacher head0.309
Teacher spread0.304 · 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
GenreEditorial

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

Citations5
Published2016
Admission routes1
Has abstractyes

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