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Record W1968073025 · doi:10.1016/j.pain.2005.09.021

Assessment of nociception in acrylamide-induced neuropathy in rats

2005· article· en· W1968073025 on OpenAlexaff
Bing Ling, Nicolas Authier, David Balayssac, Alain Eschalier, François Coudoré

Bibliographic record

VenuePain · 2005
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPotato Plant Research
Canadian institutionsCegep de Sept Iles
Fundersnot available
KeywordsNociceptionAllodyniaAcrylamideHyperalgesiaNeurotoxicityMedicineCumulative doseAnesthesiaToxicityPharmacologyChemistryInternal medicine

Abstract

fetched live from OpenAlex

Acrylamide was intraperitoneally administered to male Sprague-Dawley rats at four different doses (5, 10, 20 and 30 mg/kg) three times a week for 5 consecutive weeks. Because of motor dysfunction, the 30 mg/kg dose was not used for behavioral pain tests. Clinical status remained good throughout the experiment and no motor deficit was observed at the other doses. We showed that acrylamide administration at low doses and cumulative dose (CD) range of 35-140 mg/kg produced mechanical allodynia and rapid, marked heat (42 degrees C) and cold (10 degrees C) allodynia after tail immersion test. Mechanical and thermal hyperalgesia appeared after higher cumulative doses (70-280 mg/kg), except for cold (4 degrees C) hyperalgesia (20-80 mg/kg). All the modifications persisted throughout all study, except the mechanical hyperalgia. All the cumulative doses tested were lower than those generally reported to induce motor dysfunction (CD>250 mg/kg), confirming that CD may be considered to be a suitable index in assessing neurological signs and suggesting that early detection of acrylamide neurotoxicity would be possible using the sensory tests, especially those for detecting allodynia thresholds.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.031
GPT teacher head0.296
Teacher spread0.264 · 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 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

Citations26
Published2005
Admission routes1
Has abstractyes

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