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Record W1968547352 · doi:10.1016/j.pain.2013.05.036

Hypersensitivity to pain in congenital blindness

2013· article· en· W1968547352 on OpenAlexaff
Hocine Slimani, Sabrina Danti, Emiliano Ricciardi, Pietro Pietrini, Maurice Ptito, Ron Kupers

Bibliographic record

VenuePain · 2013
Typearticle
Languageen
FieldNeuroscience
TopicTactile and Sensory Interactions
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsAudiologyThreshold of painBlindnessSensory systemPsychologyStimulationSensory thresholdChronic painMedicinePhysical medicine and rehabilitationAnesthesiaNeuroscienceCognitive psychologyOptometry

Abstract

fetched live from OpenAlex

Vision is important for avoiding encounters with objects in the environment that may imperil physical integrity. We tested whether, in the absence of vision, a lower pain threshold would arise from an adaptive shift to other sensory channels. We therefore measured heat and cold pain thresholds and responses to suprathreshold heat stimuli in 2 groups of congenitally blind and matched normal-sighted participants. We also assessed detection thresholds for innocuous warmth and cold, and participants' attitude toward painful encounters in daily life. Our results show that, compared to sighted subjects, congenitally blind subjects have lower heat pain thresholds, rate suprathreshold heat pain stimuli as more painful, and have increased sensitivity for cold pain stimuli. Thresholds for nonpainful thermal stimulation did not differ between groups. The results of the pain questionnaires further indicated that blind subjects are more attentive to signals of external threats. These findings indicate that the absence of vision from birth induces a hypersensitivity to painful stimuli, lending new support to a model of sensory integration of vision and pain processing.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.038
GPT teacher head0.274
Teacher spread0.235 · 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 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

Citations36
Published2013
Admission routes1
Has abstractyes

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