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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 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 score0.963
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.072
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 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

Citations38
Published2017
Admission routes1
Has abstractyes

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