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Polyanalgesic Consensus Conference—2012: Recommendations on Trialing for Intrathecal (Intraspinal) Drug Delivery: Report of an Interdisciplinary Expert Panel

2012· article· en· W2100956576 on OpenAlexaff
Timothy R. Deer, Joshua P. Prager, Robert M. Levy, G.A. Burton, Eric Buchser, David Caraway, Michael J. Cousins, José De Andrés, Sudhir Diwan, Michael Erdek, Eric Grigsby, Marc A. Huntoon, Marilyn S. Jacobs, Phillip Kim, Krishna Kumar, Michael S. Leong, Liong Liem, Gladstone McDowell, Sunil Panchal, Richard Rauck, Michael Saulino, Peter S. Staats, Michael Stanton‐Hicks, Lisa Stearns, B. Todd Sitzman, Mark S. Wallace, K. Dean Willis, William Witt, Tony L. Yaksh, Nagy Mekhail

Bibliographic record

VenueNeuromodulation Technology at the Neural Interface · 2012
Typearticle
Languageen
FieldMedicine
TopicPain Management and Opioid Use
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsIntrathecalMedicineIntensive care medicineMedical emergencySurgery

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.142
metaresearch head score (Gemma)0.117
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.142
Threshold uncertainty score0.750

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1420.117
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0050.010
Bibliometrics0.0040.005
Science and technology studies0.0050.003
Scholarly communication0.0060.004
Open science0.0140.006
Research integrity0.0250.023
Insufficient payload (model declined to judge)0.0040.003

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 head0.076
GPT teacher head0.364
Teacher spread0.288 · 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 designNot applicable
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

Citations93
Published2012
Admission routes1
Has abstractno

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