MétaCan
Menu
Back to cohort
Record W2109672373 · doi:10.1155/2012/703947

The Pain Crisis: What It Is and What Can Be Done

2012· article· en· W2109672373 on OpenAlexafffund
Barry J. Sessle

Bibliographic record

VenuePain Research and Treatment · 2012
Typearticle
Languageen
FieldMedicine
TopicMusculoskeletal pain and rehabilitation
Canadian institutionsUniversity of Toronto
FundersNational Institute of Dental and Craniofacial ResearchCanadian Institutes of Health ResearchNational Institutes of Health
KeywordsMedicine

Abstract

fetched live from OpenAlex

Chronic pain is present in epidemic proportions in most countries, is often unrelieved, and has a huge socioeconomic impact. It is not just a "medical" illness but indeed is a problem that faces all healthcare professional fields. Several steps are identified to address this crisis. These include approaches to enhance pain awareness and access to timely and effective care for pain, and educational and research approaches to improve the knowledge base of healthcare professionals and students and diagnostic and management procedures for pain. Several opportunities to enhance pain understanding, access, and management are also identified.

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)0.019
metaresearch head score (Gemma)0.033
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: none
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.019
Threshold uncertainty score0.099

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0190.033
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0040.002
Bibliometrics0.0030.003
Science and technology studies0.0070.016
Scholarly communication0.0190.030
Open science0.0040.006
Research integrity0.0180.028
Insufficient payload (model declined to judge)0.0100.004

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 head0.064
GPT teacher head0.381
Teacher spread0.317 · 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
GenreCommentary

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

Citations55
Published2012
Admission routes2
Has abstractyes

Explore more

Same venuePain Research and TreatmentSame topicMusculoskeletal pain and rehabilitationFrench-language works237,207