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Record W1979082912 · doi:10.1097/bcr.0b013e3181adcd93

Burn Rehabilitation and Research: Proceedings of a Consensus Summit

2009· article· en· W1979082912 on OpenAlexaff
Reg Richard, Mary Jo Baryza, Judith A. Carr, William S. Dewey, Mary Dougherty, Lisa Forbes-Duchart, B Franzen, Tanja Healey, M. Lester, Shi-yong LI, Merilyn L. Moore, Dana Nakamura, Bernadette Nedelec, Jonathan Niszczak, Ingrid Parry, Charles D. Quick, M. Serghiou, Robert S. Ward, Linda Ware, Alan W. Young

Bibliographic record

VenueJournal of Burn Care & Research · 2009
Typearticle
Languageen
FieldMedicine
TopicBurn Injury Management and Outcomes
Canadian institutionsMcGill UniversityHealth Sciences Centre
Fundersnot available
KeywordsRehabilitationMedicineSummitBurn injuryPhysical therapyPhysical medicine and rehabilitationMedical emergencySurgery

Abstract

fetched live from OpenAlex

Burn rehabilitation is an essential component of successful patient care. In May 2008, a group of burn rehabilitation clinicians met to discuss the status and future needs of burn rehabilitation. Fifteen topic areas pertinent to clinical burn rehabilitation were addressed. Consensus positions and suggested future research directions regarding the physical aspects of burn rehabilitation are shared.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.188
metaresearch head score (Gemma)0.150
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.188
Threshold uncertainty score0.996

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1880.150
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0030.004
Bibliometrics0.0070.007
Science and technology studies0.0140.003
Scholarly communication0.0080.007
Open science0.0070.025
Research integrity0.0210.023
Insufficient payload (model declined to judge)0.0050.002

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 head0.091
GPT teacher head0.431
Teacher spread0.340 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations144
Published2009
Admission routes1
Has abstractyes

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