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Record W3194589633 · doi:10.29327/multiscience.2021002

pH e condutividade do cloridrato de procaína em diferentes concentrações utilizadas em terapia neural

2020· article· pt· W3194589633 on OpenAlexaboutno aff
Leonardo Rocha Vianna, Bruna Aparecida Lima Gonçalves

Bibliographic record

VenueMultidisciplinary Science Journal · 2020
Typearticle
Languagept
FieldChemistry
TopicChemical Thermodynamics and Molecular Structure
Canadian institutionsnot available
Fundersnot available
KeywordsPhysicsHumanitiesPhilosophy

Abstract

fetched live from OpenAlex

O cloridrato de procaína é um anestésico local utilizado na terapia neural com resultados surpreendentes em diferentes tipos de pacientes e tratamentos. A terapia neural foi desenvolvida principalmente, pelos irmãos alemães Huneke e é praticada há mais de 100 anos. Em 1940, Ferdinand Huneke verificou o desaparecimento súbito de uma dor no ombro de um paciente após aplicação em uma cicatriz de osteomielite na perna. Esse tratamento é difundido na Alemanha, Áustria, Argentina, Canada, Colômbia, Costa Rica, Cuba, Espanha, Estados Unidos, México, Suíça e hoje vem se difundido também no Brasil e vários outros países. Tem reconhecimento internacional como método curativo e eficaz em muitas enfermidades. Para conhecer melhor as características da principal substância e das soluções utilizadas na terapia neural, bem como a velocidade de resposta do organismo após sua aplicação, foram realizadas experimentos para estabelecer o comportamento elétrico de seus componentes. O objetivo desse trabalho é demonstrar e avaliar o pH e a condutividade de diferentes concentrações de soluções de cloridrato de procaína.

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), Science and technology studies, Scholarly communication, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.470
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0020.001
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0010.000

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 head0.023
GPT teacher head0.288
Teacher spread0.265 · 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 designBench or experimental
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

Citations4
Published2020
Admission routes1
Has abstractyes

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