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Record W3157029511 · doi:10.5772/intechopen.96676

Assessment and Management of Pain in Palliative Care

2021· book-chapter· en· W3157029511 on OpenAlexaboutno aff
Sonika Charak, Robin George Thattil, Chandra Mohan Srivastava, Prabhu Prasad Das, Manish Shandilya

Bibliographic record

VenueIntechOpen eBooks · 2021
Typebook-chapter
Languageen
FieldMedicine
TopicPain Management and Opioid Use
Canadian institutionsnot available
FundersNational Brain Research CentreAmity University
KeywordsNociceptionMedicineNeuropathic painPalliative carePain assessmentCancer painVisceral painDistressDiseaseQuality of life (healthcare)Referred painPain managementPhysical therapyCancerAnesthesiaInternal medicineNursingReceptorClinical psychology

Abstract

fetched live from OpenAlex

Palliative care is an essential component in any disease management. Pain assessment acts as the connecting link between the nerves, brain and spinal cord. Classification and assessment of the pain have great significance in controlling the pain-related symptoms. Pain is broadly divided into three types nociceptive, neuropathic and mixed depending upon the damage caused. Nociceptive pain is caused due to the stimulation of the pain receptors in the tissues and is further divided into visceral and somatic depending on the pain site. Neuropathic pain arises when the nervous system gets damaged or start dysfunctioning. Cancer pain assessment includes several factors like the site, intensity, syndrome, timing and temporal variation of pain. Edmonton staging system for cancer pain prognostic is widely used for pain management includes emotional/psychological distress cognitive impairment caused by pain. A comprehensive understanding of pain assessment will help in enhancing the quality of life of the patients.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.874
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.032
GPT teacher head0.316
Teacher spread0.285 · 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.

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

Citations1
Published2021
Admission routes1
Has abstractyes

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