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Record W3156505412 · doi:10.5772/intechopen.93940

Pain Management in Older Persons

2021· book-chapter· en· W3156505412 on OpenAlexaboutno aff
Dabota Yvonne Buowari

Bibliographic record

VenueIntechOpen eBooks · 2021
Typebook-chapter
Languageen
FieldMedicine
TopicPain Management and Opioid Use
Canadian institutionsnot available
Fundersnot available
KeywordsPain catastrophizingActivities of daily livingMedicineRating scalePhysical therapyPain managementMcGill Pain QuestionnairePain assessmentChronic painAffect (linguistics)OsteoarthritisCancer painPsychologyPhysical medicine and rehabilitationVisual analogue scaleCancerAlternative medicine

Abstract

fetched live from OpenAlex

Pain is a common symptom in the elderly and it is problematic and distressful especially if the polder person is dependent on a caregiver. Pain keeps the sufferer uncomfortable and can affect the person from carrying out daily activities and tasks especially activities of daily living. Pain in the older person may be acute or chronic. Some of the causes of pain in the elderly are neuralgia, musculoskeletal dysfunction especially osteoarthritis, emotional and mental problems, cancer and several other causes. The assessment of pain in the elderly is done using validated pain assessment tools such as the visual analogue scale, verbal rating scales, numeric rating scales, McGill pain assessment questionnaire, pain attitudes, brief pain inventory, and geriatric pain measure. Management of pain in older persons involves non-pharmacological and pharmacological methods. There are some barriers and challenges of pain management in the elderly and also consequences when pain is not properly managed or not managed at all in an older person.

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)0.000
metaresearch head score (Gemma)0.001
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: none
Teacher disagreement score0.023
Threshold uncertainty score0.075

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0230.010

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 head0.022
GPT teacher head0.265
Teacher spread0.243 · 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

Citations4
Published2021
Admission routes1
Has abstractyes

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