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Assessment

2019· book-chapter· en· W4250809690 on OpenAlexaboutno aff
Andrew Davies

Bibliographic record

VenueOxford University Press eBooks · 2019
Typebook-chapter
Languageen
FieldMedicine
TopicPain Management and Opioid Use
Canadian institutionsnot available
Fundersnot available
KeywordsMedicinePain assessmentBreakthrough PainIntensive care medicineCancer painPhysical therapyPatient assessmentPain managementAlternative medicinePathology

Abstract

fetched live from OpenAlex

Successful management of breakthrough pain depends on adequate assessment and adequate re-assessment. The assessment of pain primarily depends on basic clinical skills, that is, taking a detailed history and performing a thorough examination. Inadequate assessment may lead to ineffective or even inappropriate treatment. The objectives of assessment are to determine the aetiology and pathophysiology of the pain, and factors that indicate or contraindicate particular treatments. It is important to differentiate patients with uncontrolled background pain experiencing transient exacerbations of that pain, from patients with controlled background pain experiencing episodes of breakthrough pain. Inadequate reassessment may lead to the continuance of ineffective or inappropriate treatment. A number of different tools have been developed for the assessment of cancer-related pain. Those focusing on breakthrough pain include: the Breakthrough Pain Questionnaire, the Alberta Breakthrough Pain Assessment Tool, and the Breakthrough Pain Assessment Tool.

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)0.007
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: Other · Consensus signal: Other
Teacher disagreement score0.146
Threshold uncertainty score0.000

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0020.002
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.1460.079

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 head0.023
GPT teacher head0.234
Teacher spread0.211 · 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
GenreOther

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
Published2019
Admission routes1
Has abstractyes

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