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Record W2023402503 · doi:10.1016/s0304-3959(00)00348-1

The formalin test in the mouse: a parametric analysis of scoring properties

2000· article· en· W2023402503 on OpenAlexafffund
Ghada-Maria Saddi, Frances V. Abbott

Bibliographic record

VenuePain · 2000
Typearticle
Languageen
FieldMedicine
TopicPain Mechanisms and Treatments
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of CanadaMcGill University
KeywordsTest (biology)Parametric statisticsMedicineStatisticsMathematicsBiology

Abstract

fetched live from OpenAlex

We investigated the scoring properties of the mouse formalin test using the time-sampling method recently developed for infant and adult rats. Formalin was injected under the plantar surface of one rear paw (10 microl, 1-8%), and pain behaviours (paw favouring, lifting and licking) and behavioural state were recorded. Correlational and regression analyses indicated that scores composed of combinations of all three pain behaviours, either summed or weighted, provided less variable indices of pain than licking alone. The maximum percent effect (MPE(50); i.e. pain behaviour 50% of the time) for the log formalin concentration-effect curves was 3-4% in both phases. Habituation to the test environment prior to testing did not alter the MPE(50)s, but slopes were lower in unhabituated mice, dramatically increasing the size of the confidence interval. Formalin dose-dependently reduced locomotion, rearing and sniffing in both the first phase and the early part of the second phase. The combination measures were sensitive to morphine (2-8 mg/kg), amphetamine (1-4 mg/kg), dipyrone (50-200 mg/kg), xylazine (0.25-1 mg/kg), and acepromazine (0.25-1 mg/kg), and resistant to diazepam (0.5-2 mg/kg), pimozide (0.05-0.25 mg/kg), pentobarbital (10 and 15 mg/kg) and indomethacin (2-8 mg/kg). Decreased pain was correlated with increased motor activity for morphine and amphetamine, and with decreased activity for xylazine and acepromazine; dipyrone and indomethacin did not alter activity levels.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.337
Threshold uncertainty score0.106

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.024
GPT teacher head0.258
Teacher spread0.233 · 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 designObservational
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

Citations77
Published2000
Admission routes2
Has abstractyes

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