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Record W2795084480 · doi:10.4081/reumatismo.2018.1031

The real evidence for polymyalgia rheumatica as a paraneoplastic syndrome

2018· review· en· W2795084480 on OpenAlexaboutno aff
Sara Müller, Samantha Hider, Toby Helliwell, Richard Partington, Christian Mallen

Bibliographic record

VenueReumatismo · 2018
Typereview
Languageen
FieldMedicine
TopicVasculitis and related conditions
Canadian institutionsnot available
FundersNational Institute for Health and Care Research
KeywordsMedicinePolymyalgia rheumaticaDiseaseCancerProstate cancerMEDLINEEvidence-based medicineLymphomaDermatologyInternal medicinePathologyAlternative medicineGiant cell arteritisVasculitis

Abstract

fetched live from OpenAlex

The aim of this study was to systematically consider the evidence for polymyalgia rheumatica (PMR) as a paraneoplastic disease. A systematic review of Medline and Embase was conducted from their inception to February 2017. Risk of bias was assessed using the Newcastle-Ottawa tool. Data were extracted regarding the PMR-cancer association, the types of cancer associated with PMR and the presentation of PMR patients subsequently diagnosed with cancer. Twenty-three full text articles were reviewed from the 1174 unique references identified in the search. Nine articles were included in the final review. There was some evidence of an association between PMR and cancer in the short-term (first 6 to 12 months after diagnosis), but no evidence of an association after this time. Limited evidence suggests that lymphoma, prostate and haematological cancers may be those cancers more commonly diagnosed in those with PMR. There was little evidence to suggest what presenting features may be associated with the development of cancer. There is little evidence of PMR as a true paraneoplastic disease. However, there is reason to be cautious when making the diagnosis of PMR. Clinicians should be aware of this potential association both prior to making a diagnosis and throughout the course of the condition.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.903
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0000.002

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 head0.083
GPT teacher head0.391
Teacher spread0.308 · 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 designOther design
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

Citations43
Published2018
Admission routes1
Has abstractyes

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