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Record W2411387225 · doi:10.1227/neu.0000000000000950

The Burden of Spinal Disorders in the Elderly

2015· review· en· W2411387225 on OpenAlex
Robert Waldrop, Joseph Cheng, Clinton J. Devin, Matthew J. McGirt, Michael G. Fehlings, Sigurd Berven

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueNeurosurgery · 2015
Typereview
Languageen
FieldMedicine
TopicMusculoskeletal pain and rehabilitation
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMedicineDisease burdenHealth careBurden of diseaseQuality of life (healthcare)PopulationDiseaseEnvironmental healthPhysical therapyPathologyNursing

Abstract

fetched live from OpenAlex

Disorders of the spine are common and have a significant and measurable burden on affected patients and on our healthcare economy. The burden of spinal disorders encompasses metrics such as the prevalence of spinal disorders, the impact of spinal disorders on health-related quality of life, and the use of resources associated with the operative and nonoperative management of spinal disorders. Measurement of the burden of spinal disorders is important in prioritizing the distribution of limited resources within our healthcare economy. In 1998, the Priority Setting Committee of the Institute of Medicine concluded that in defining health priorities for research and funding, the burden of disease and impact on the health of the population should be the primary determinants of resource allocation. The purpose of this article is to report metrics comprising the burden of spinal disorders, with a focus on the significant and growing burden of spinal disorders in our elderly population, and to demonstrate that allocation of resources to the management of spinal disorders should be a priority for our healthcare economy.

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.

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)0.001
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 score0.991
Threshold uncertainty score0.338

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
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 head0.040
GPT teacher head0.349
Teacher spread0.309 · 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