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Record W4220731398 · doi:10.1016/s2468-2667(22)00063-9

Closing the global pain divide: balancing access and excess

2022· letter· en· W4220731398 on OpenAlexaboutno aff
Felícia Marie Knaul, William E. Rosa, Héctor Arreola‐Ornelas, Renu Sara Nargund

Bibliographic record

VenueThe Lancet Public Health · 2022
Typeletter
Languageen
FieldMedicine
TopicOpioid Use Disorder Treatment
Canadian institutionsnot available
FundersNational Cancer Institute
KeywordsClosing (real estate)MedicineComputer scienceBusiness

Abstract

fetched live from OpenAlex

Access to pain relief medication is one of the most heinous, hidden inequities in global health. The Lancet Commission on global access to palliative care and pain relief called on health systems and their leaders, including academics, to address the so-called 10–90 pain divide—ie, that the richest 10% of countries possess 90% of distributed morphine-equivalent opioids.1 In an accompanying Article published in The Lancet Public Health, Chengsheng Ju and colleagues contribute evidence that supports the Commission's findings: between 2015 and 2019, disparities in opioid analgesic distribution persisted, despite small increases in regional and global opioid distribution, reflecting the inadequate access to opioid analgesics in countries with a low consumption.

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)0.008
metaresearch head score (Gemma)0.023
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: Commentary · Consensus signal: Commentary
Teacher disagreement score0.024
Threshold uncertainty score0.082

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.023
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.004
Science and technology studies0.0030.013
Scholarly communication0.0150.026
Open science0.0020.018
Research integrity0.0040.007
Insufficient payload (model declined to judge)0.0240.003

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 head0.098
GPT teacher head0.372
Teacher spread0.273 · 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
GenreCommentary

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

Citations38
Published2022
Admission routes1
Has abstractyes

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