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Record W4414436462 · doi:10.1213/xaa.0000000000002053

Appropriate Selection or Poor Triaging: Assessment of Patient Profiles Presenting to a Neuromodulation for Pain Program at a Tertiary Academic Center

2025· article· en· W4414436462 on OpenAlexaff
Rasheeda Darville, Abeer Alomari, Danielle Alvares, Victoria Bains, Emma Robertson, Pranab Kumar, Yasmine Hoydonckx, Ehtesham Baig, Ryan S. D’Souza, Anuj Bhatia

Bibliographic record

VenueA&A Practice · 2025
Typearticle
Languageen
FieldMedicine
TopicPain Management and Treatment
Canadian institutionsToronto Western HospitalUniversity of Toronto
Fundersnot available
KeywordsNeuromodulationReferralNeuropathic painSpinal cord stimulationOpioidQuality of life (healthcare)Chronic pain

Abstract

fetched live from OpenAlex

Spinal cord stimulation (SCS) is effective for some pain syndromes, but inappropriate referrals can frustrate patients and misuse resources. This retrospective study reviewed 370 patients referred to a Neuromodulation for Pain Program (NPP) from July 2022 to June 2024. Of these, 241 were deemed appropriate for SCS. The SCS-appropriate group had higher pain intensity, more neuropathic pain, and lower opioid use. Inappropriate referrals were often from family physicians. Key factors associated with inappropriateness included low pain intensity, lack of neuropathic features, and high opioid use. Enhancing referral quality through targeted education may improve care and system efficiency.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.628
Threshold uncertainty score0.446

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
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)0.0000.000

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 head0.030
GPT teacher head0.392
Teacher spread0.362 · 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

Citations1
Published2025
Admission routes1
Has abstractyes

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