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Record W1999095486 · doi:10.1097/bcr.0000000000000027

Toward Targeted Early Burn Care

2014· article· en· W1999095486 on OpenAlexaboutno aff
Anne‐Françoise Rousseau, Paul Massion, Alexis Laungani, Jean-Luc Nizet, Pierre Damas, Didier Ledoux

Bibliographic record

VenueJournal of Burn Care & Research · 2014
Typearticle
Languageen
FieldMedicine
TopicBurn Injury Management and Outcomes
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineResuscitationRespondentIntensive care medicineIntensive careEmergency medicine

Abstract

fetched live from OpenAlex

During the year 2011, a survey was performed to describe current practices throughout Europe regarding three critical issues of acute burn care, namely fluid resuscitation, nutrition, and burn wound excision strategy. Thirty-eight questionnaires returned by burn centres from 17 different European countries were analyzed. The survey shows that Parkland remains the most commonly used formula to determine fluid needs in adults. All respondent centers use urine output to guide fluid resuscitation. While early excision of deep burns is the rule among centers, burn depth assessment by laser Doppler imaging is used in only a few centers. Indirect calorimetry and Toronto formula to estimate energy requirements do not have unanimous backing from respondents. Current literature encourages clinicians to move forward targeted and individualized therapies using a bundle of basic and advanced hemodynamic parameters, indirect calorimetry, and laser Doppler imaging. The results of this study suggest that such an approach is not common yet, and reinforce the subsequent need for large clinical trials that would evaluate the impact of such guided therapies to provide recommendations with a significant level of evidence.

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)0.002
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.550
Threshold uncertainty score0.589

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.066
GPT teacher head0.385
Teacher spread0.319 · 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.

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

Citations23
Published2014
Admission routes1
Has abstractyes

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