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Record W2996825319 · doi:10.3390/jcm9010042

Surgical Treatment for the Ankle and Foot in Patients with Rheumatoid Arthritis: A Systematic Review

2019· review· en· W2996825319 on OpenAlexaboutno aff
Ana Belén Ortega‐Ávila, Antonio Moreno-Velasco, Pablo Cervera‐Garvi, Magdalena Martínez-Rico, Esther Chicharro‐Luna, Gabriel Gijón-Noguerón

Bibliographic record

VenueJournal of Clinical Medicine · 2019
Typereview
Languageen
FieldMedicine
TopicFoot and Ankle Surgery
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineRandomized controlled trialCINAHLRheumatoid arthritisPhysical therapyAnkleCohortFoot (prosody)Cohort studyPopulationMEDLINESystematic reviewSurgeryInternal medicinePsychological intervention

Abstract

fetched live from OpenAlex

The aim was to identify effective surgical treatments for patients with rheumatoid arthritis in the foot and/or ankle. A systematic review of the literature was conducted via a data search of the PubMed, Scopus, Cumulative Index to Nursing and Allied Health Literature (CINAHL), and PROSPERO databases, from their inception until June 2019. Only non-randomized controlled trials and cohort studies were included in this review. Two of the present authors independently assessed the quality of each study and extracted the relevant data. A quality assessment of all articles was performed using the methodological index for non-randomized studies criteria. In addition, the Newcastle Ottawa scale was used for cohort studies. Thirteen studies met the inclusion criteria (five cohort studies and eight NRCTs). The total population considered was 923 patients (570 patients had RA), with a mean age of 58.8 years. Regarding the risk of bias, both the NRCTs and the cohort studies had a moderate level of quality. Despite the relatively low quality of these studies, surgical treatment for the foot and/or ankle is shown to reduce pain and improve functionality in patients with RA, in the short term (6-12 months).

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)0.005
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.461
Threshold uncertainty score0.733

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0110.001
Bibliometrics0.0000.000
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 head0.133
GPT teacher head0.439
Teacher spread0.306 · 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 designSystematic review
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

Citations5
Published2019
Admission routes1
Has abstractyes

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