MétaCan
Menu
Back to cohort
Record W2616975411 · doi:10.1007/s40122-017-0072-7

Chronic Non-Cancer Pain Management Capacity in Karachi

2017· review· en· W2616975411 on OpenAlexaff
S. Fatima Lakha, Peter Pennefather, Mubina Agboatwala, Safia Zafar Siddiqui, Hanan E. Badr, Angela Mailis

Bibliographic record

VenuePain and Therapy · 2017
Typereview
Languageen
FieldMedicine
TopicDiabetes Management and Education
Canadian institutionsCentre for Disability Prevention and RehabilitationUniversity of Toronto
Fundersnot available
KeywordsComparabilityCancer painMegacityDeveloping countryMedicinePain managementBusinessPublic healthAlternative medicineEconomic growthPolitical sciencePhysical therapyNursingEconomics

Abstract

fetched live from OpenAlex

Chronic non-cancer pain (CNCP) affects people everywhere in the world, but people in developing countries have far less access to therapies that provide relief. There are often missed opportunities to implement these therapies. Karachi shares many characteristics with megacities of the global south and represents Pakistan in the global city league. This review informs readers about the availability of health management and pain services for CNCP in Karachi, and their comparability to those found in other global cities. The literature about CNCP and its management in Karachi and Pakistan is scarce. Nevertheless, some conclusions can be made. In order to inform readers based in other global cities, a brief review of the current health system and pain services in Karachi and Pakistan are discussed together with barriers that impede pain service outputs. The present review employs vignettes to illustrate typical experiences of CNCP patients seeking pain management services in three sectors: public, charitable, and private institutions.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.983
Threshold uncertainty score0.807

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 head0.133
GPT teacher head0.378
Teacher spread0.245 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations6
Published2017
Admission routes1
Has abstractyes

Explore more

Same venuePain and TherapySame topicDiabetes Management and EducationFrench-language works237,207