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Record W2121748926 · doi:10.4076/1752-1947-3-8992

Diagnostic use of infrared thermography in a patient with chronic pain following electrocution: a case report

2009· article· en· W2121748926 on OpenAlexaffabout
John Jarrell, Chris C. Spanswick

Bibliographic record

VenueJournal of Medical Case Reports · 2009
Typearticle
Languageen
FieldMedicine
TopicInfrared Thermography in Medicine
Canadian institutionsChevron (Canada)Alberta Health ServicesUniversity of Calgary
Fundersnot available
KeywordsElectrocutionMedicineThermographyPregabalinChronic painPain medicineAnesthesiaFoot (prosody)SurgeryPhysical therapyAnesthesiologyEmergency medicine

Abstract

fetched live from OpenAlex

Survival after severe electrocution is uncommon but chronic pain after such trauma is rare. We present a case report of an individual in whom the only modality providing objective evidence of pain related injury was infrared thermography. A 35-year-old Caucasian woman presented to the Calgary Health Region Chronic Pain Centre with severe pain in her left hand and foot following electrocution. All previous clinical and neurological testing had been normal. Infrared thermography demonstrated a significant reduction in temperature in the regions affected on her left hand and foot. Pain was reduced with the use of pregabalin but without changes to thermal differences in the affected limbs. It would appear from this case report that infrared thermography may be of use in the documentation of abnormalities associated with chronic pain following survival after electrocution. Pregabalin may be of benefit in pain reduction after electrocution.

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)0.001
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0030.002
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0060.003
Insufficient payload (model declined to judge)0.0030.001

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 head0.010
GPT teacher head0.269
Teacher spread0.259 · 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 designCase report
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

Citations6
Published2009
Admission routes2
Has abstractyes

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