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Record W4385637986 · doi:10.1097/pr9.0000000000001090

Understanding the pain experience of lionfish envenomation

2023· article· en· W4385637986 on OpenAlexafffund
Stephanie Mouchbahani-Constance, Manon Choinière, Reza Sharif‐Naeini

Bibliographic record

VenuePAIN Reports · 2023
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicMarine Invertebrate Physiology and Ecology
Canadian institutionsUniversité de MontréalMcGill University
FundersCanadian Institutes of Health Research
KeywordsStingEnvenomationMedicineAcute painFisheryBiologyAnesthesiaVenomEngineering

Abstract

fetched live from OpenAlex

Introduction: Stings from the lionfish (Pterois volitans) constitute one of the most painful wounds in the ocean. This species has invaded the Atlantic coast of the United States, Gulf of Mexico, Caribbean, and Mediterranean Sea. In addition to its ecological impact on local fish populations, stings from the lionfish pose a medical problem because of the debilitating nature of the pain they produce. However, there are no studies examining the human pain experience of lionfish stings. Objective: To characterize the various aspects of the pain experience following a lionfish sting. Methods: We developed a pain questionnaire that includes validated scales used with patients having acute or chronic pain to understand the pain variability, as well as the use of health care resources and treatments. Results: We provide the first study of the pain experience from lionfish stings. Here, we show that the pain is intense from the start and peaks approximately 1 hour later, resolving itself in 7 days for most victims. Furthermore, pain intensity can be influenced by several factors, including (1) age of the victim, where older victims experience significantly higher pain intensities, (2) the number of spines involved, (3) and whether infection occurred at the injury site. However, pain intensity was not different between male and female participants. Conclusion: These findings will inform the medical community on the pain experience and can be used by local authorities to better appreciate the impact of lionfish envenomations to develop programs aimed at curtailing the expansion of the lionfish.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.147
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.066
GPT teacher head0.232
Teacher spread0.166 · 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 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

Citations3
Published2023
Admission routes2
Has abstractyes

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