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Record W2107660201 · doi:10.4314/eamj.v77i9.46696

Use of a simple pain model to evaluate analgesic activity of ibuprofen versus paracetamol

2009· article· en· W2107660201 on OpenAlexaff
I Lala, Peter Leech, Lori Montgomery, K Bhagat

Bibliographic record

VenueEast African Medical Journal · 2009
Typearticle
Languageen
FieldMedicine
TopicInflammatory mediators and NSAID effects
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMedicineIbuprofenAnalgesicAnesthesiaSore throatTolerabilitySwallowingAdverse effectSurgeryInternal medicinePharmacology

Abstract

fetched live from OpenAlex

OBJECTIVE: To evaluate the analgesic activity of ibuprofen against paracetamol using a simple pain model. DESIGN: A double-blind study. SETTING: Twenty general practitioners in Harare, Zimbabwe. PATIENTS: Adults with acute sore throat of a maximum of two days' duration. INTERVENTIONS: One hundred and thirteen patients with acute pain associated with tonsillo-pharyngitis randomly received either 400 mg ibuprofen or 1000 mg paracetamol. The study design included repeated administration up to 48 hours to assess tolerability. MAIN OUTCOME MEASURES: At hourly intervals for six hours after the first dose of treatment, the patients evaluated pain intensity on swallowing, difficulty in swallowing and global pain relief according to visual analogue scales. RESULTS: Ibuprofen 400 mg was significantly more effective than paracetamol 1000 mg in all three ratings, at all time-points for pain intensity and difficulty in swallowing, and from two hours onwards for pain relief. There were no serious adverse effects and no statistically significant difference in the incidence of adverse effects in the two treatment groups. CONCLUSIONS: Sore throat pain provided a sensitive model to assess the analgesic efficacy of class I analgesics and discriminated between the analgesic efficacy of ibuprofen and paracetamol.

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)0.003
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.849
Threshold uncertainty score0.629

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.057
GPT teacher head0.328
Teacher spread0.271 · 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 designOther design
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

Citations12
Published2009
Admission routes1
Has abstractyes

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