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Record W2752322426 · doi:10.2217/nmt-2017-0014

Treatment Gaps in Parkinson’s Disease Care in the Philippines

2017· review· en· W2752322426 on OpenAlexaff
Roland Dominic G. Jamora, Janis M. Miyasaki

Bibliographic record

VenueNeurodegenerative Disease Management · 2017
Typereview
Languageen
FieldMedicine
TopicParkinson's Disease Mechanisms and Treatments
Canadian institutionsCentre for Movement DisordersUniversity of Alberta
Fundersnot available
KeywordsHealth careMedicineGovernment (linguistics)DiseaseDeveloping countryPopulationEpidemiologyBusinessEconomic growthEnvironmental healthEconomics

Abstract

fetched live from OpenAlex

Neurological services and resources are scarce in low-income and developing countries, such as the Philippines. We looked into the treatment gaps in Parkinson's disease (PD) care in the Philippines in the following areas: epidemiology, healthcare, financial coverage, pharmacotherapy, surgical treatment and manpower. We collected relevant data on the above-mentioned areas. There is no available Philippine data on PD prevalence. Philippine healthcare is paid through user fees at the point of service. The average consultation fee in Manila ranges from US$10.57-31.74. The average minimum daily wage is US$9.39-10.17. Philippine healthcare is devolved to the local government units. Deep brain stimulation surgery is only available in Manila. Most PD medications are available in the Philippines. There are only nine movement disorder specialists for a population of 100.98 million. Gaps and challenges in PD care in the Philippines still exist.

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)0.002
metaresearch head score (Gemma)0.003
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: Review · Consensus signal: Review
Teacher disagreement score0.006
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.003
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.072
GPT teacher head0.358
Teacher spread0.286 · 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
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

Citations38
Published2017
Admission routes1
Has abstractyes

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