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Record W2053956382 · doi:10.1097/spc.0b013e32835017e7

Interventional management of cancer pain

2012· review· en· W2053956382 on OpenAlexfundno aff
Arun Bhaskar

Bibliographic record

VenueCurrent Opinion in Supportive and Palliative Care · 2012
Typereview
Languageen
FieldMedicine
TopicPain Management and Opioid Use
Canadian institutionsnot available
FundersAstellas PharmaCanadian Pain SocietyBritish Pain Society
KeywordsMedicineCancer painCordotomyPalliative careInterventional pain managementPercutaneousRadiofrequency ablationCancerIntensive care medicineSurgeryAnesthesiaPain managementAblationInternal medicineNursing

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: Interventional techniques were the mainstay for cancer pain management before the WHO ladder and opioids were freely available. The three-step WHO ladder has its limitations, and cancer pain is often under treated. Advances in treatment options mean that cancer patients are living longer and pain interventions may have a role to play even early in the cancer diagnosis for better quality of analgesia. The role of high doses of opioids in pain management is also currently under scrutiny. RECENT FINDINGS: Recent advances in intrathecal analgesia, radiofrequency techniques, both in tumour ablation and neurotomies, are being widely used for palliation. Vertebroplasty techniques have been used not only for pain relief, but also for stabilization. Improved imaging and thoracoscopic techniques have made coeliac plexus and splanchnic blockade safer and more efficacious. There has been recent interest in percutaneous cordotomy with newer techniques using computed tomography/MRI and endoscopy guidance. Percutaneous electrical nerve stimulation and 8% capsaicin patches have been successfully used for managing neuropathic pain in cancer. SUMMARY: Interventions form an integral part in providing pain relief in complex cancer pains. Oncologists and palliative care physicians are to be educated on the usefulness and timing of interventions in the management of complex cancer pain.

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: Review · Consensus signal: Review
Teacher disagreement score0.857
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 head0.207
GPT teacher head0.476
Teacher spread0.269 · 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
GenreReview

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

Citations49
Published2012
Admission routes1
Has abstractyes

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