MétaCan
Menu
← Back to cohort

Design and optimization of radio-frequency (RF) ablation probes for pain therapy

2015· article· en· W1952316943 on OpenAlexaff
Shashwat Sharma, Alon Ludwig, Costas D. Sarris

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMedicine
TopicSpine and Intervertebral Disc Pathology
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsRadio frequencyAblationFacet (psychology)RF probeMedicineOsteoarthritisRadiofrequency ablationBiomedical engineeringRadiologyComputer scienceRF power amplifierAmplifierTelecommunicationsPathologyCardiology

Abstract

fetched live from OpenAlex

Chronic back pain results most frequently from degenerative osteoarthritis of the joints of the vertebra, called the facet joints, where much of the movement of the spine occurs. When conservative therapy fails, a commonly performed procedure is radio-frequency (RF) ablation of the nerve supply to the affected facet joints. This procedure involves the use of RF ablation probes that can be inserted into the tissue surrounding the targeted area. Commercially available probe designs are akin to sharp needles, expected to create large field intensities at their tip (E. R. Cosman, Jr. and E. R. Cosman, Sr., Pain Medicine, vol. 6, no. 6, 2005 ). However, these knife-edge probes do not offer much control over the induced temperature profile (“RF lesion”). Hence, this procedure is accompanied by a high risk of damaging the surrounding tissue, including motor nerves.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.078
GPT teacher head0.312
Teacher spread0.234 · 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 designBench or experimental
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

Citations0
Published2015
Admission routes1
Has abstractyes

Explore more

Same topicSpine and Intervertebral Disc Pathology→French-language works237,207→