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Record W3201058901 · doi:10.33137/cpoj.v4i2.35972

(IN) ACCESS TO ARTIFICIAL LIMBS: THE PATIENT’S PERSPECTIVE ACCORDING TO THE WAR AMPS OF CANADA

2021· article· en· W3201058901 on OpenAlexaffvenueabout
Annelise Petlock, Keana DiMario

Bibliographic record

VenueCanadian Prosthetics & Orthotics Journal · 2021
Typearticle
Languageen
FieldMedicine
TopicTotal Knee Arthroplasty Outcomes
Canadian institutionsMining Association of Canada
Fundersnot available
KeywordsPerspective (graphical)Artificial intelligenceComputer science

Abstract

fetched live from OpenAlex

Funding agencies, both public and private, do not adequately meet the needs of Canadian amputees. This often leaves amputees without access to appropriate prosthetic care due to outstanding balances that are to be paid out of pocket, or by charitable organizations. There are several factors that result in these inadequacies. As healthcare is a provincial responsibility in Canada, provinces and territories have the authority to create individual public regimes, each with their own weaknesses. In fact, there are a few provincial regimes which do not include prosthetic funding at all. Private healthcare is meant to offset the remaining balance; however, their lack of knowledge regarding amputation has resulted in the creation of policies with ambiguous language, limiting the funding available for prosthetic care. Attitudinal barriers and missed legislative opportunities further exacerbate the shortcomings of prosthetic funding provided by public and private funding agencies, requiring action.
 Article PDF Link:https://jps.library.utoronto.ca/index.php/cpoj/article/view/35972/28331
 How To Cite: Petlock A, DiMario K. (In) Access to artificial limbs: the patient’s perspective according to the WAR AMPS of Canada. Canadian Prosthetics & Orthotics Journal. 2021; Volume 4, Issue 2, No.13. https://doi.org/10.33137/cpoj.v4i2.35972
 Corresponding Author: Annelise PetlockThe War Amps Advocacy Program/ 2827 Riverside Drive Ottawa, Ontario, Canada.E-Mail: annelise.petlock@waramps.caORCID ID: https://orcid.org/0000-0002-7273-4527

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.710
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.018
GPT teacher head0.269
Teacher spread0.252 · 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 designObservational
Domainnot available
GenreEmpirical

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

Citations0
Published2021
Admission routes3
Has abstractyes

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