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Record W3189691371 · doi:10.5430/jnep.v11n12p46

Conceptualizing breakthrough pain

2021· article· en· W3189691371 on OpenAlexvenueno aff
Leslie Narain, Rida Naeem, Apurva Nemala, Daniel F. Linder, Zhuo Sun, Lufei Young

Bibliographic record

VenueJournal of Nursing Education and Practice · 2021
Typearticle
Languageen
FieldMedicine
TopicOpioid Use Disorder Treatment
Canadian institutionsnot available
Fundersnot available
KeywordsConceptual modelChronic painFoundation (evidence)PsychologyPain managementBreakthrough PainMedicineOpioidPhysical therapyPsychiatryComputer sciencePolitical science

Abstract

fetched live from OpenAlex

The concept of breakthrough pain (BTP) is examined through the development of a conceptual model with a long-term goal of positively impacting the management of chronic pain patients who experience BTP when hospitalized. The model is based on a 2008 Health Economic Model of Breakthrough Pain developed by Abernethy, Wheeler, and Fortner, which will be referred to as the parent model. The conceptual model of BTP, titled, Novel Conceptual Model of Breakthrough Pain (NCMBP) shares a similar structure in regards to the relationships of major constructs. Like the parent model, the NCMBP is based on three major constructs which are analyzed and explained further with associated concepts. The NCMBP is primarily concerned with the importance of a pain management plan and the endpoint result of patient-perceived analgesia. The NCMBP is viewed as a necessary foundation for continuing safe and effective pain management in the setting of a current opioid overdose epidemic in the United States. The structure and conceptual relationships of the NCMBP are preliminary and will continue to undergo revision as conduction of research is attempted to test the model.

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)0.004
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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.010
Threshold uncertainty score0.052

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0030.002
Science and technology studies0.0020.012
Scholarly communication0.0040.007
Open science0.0020.004
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0030.000

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 head0.079
GPT teacher head0.444
Teacher spread0.365 · 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 designTheoretical or conceptual
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
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Nursing Education and Practice→Same topicOpioid Use Disorder Treatment→French-language works237,207→