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An algorithm recommendation for the management of knee osteoarthritis in Europe and internationally: A report from a task force of the European Society for Clinical and Economic Aspects of Osteoporosis and Osteoarthritis (ESCEO)

2014· article· en· W2164287243 on OpenAlex

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

VenueSeminars in Arthritis and Rheumatism · 2014
Typearticle
Languageen
FieldMedicine
TopicOsteoarthritis Treatment and Mechanisms
Canadian institutionsUniversité de Montréal
FundersMedical Research CouncilNational Institute for Health and Care Research
KeywordsMedicinePsychological interventionOsteoarthritisAlgorithmPhysical therapyMedical prescriptionReferralAlternative medicineFamily medicinePathology

Abstract

fetched live from OpenAlex

OBJECTIVES: Existing practice guidelines for osteoarthritis (OA) analyze the evidence behind each proposed treatment but do not prioritize the interventions in a given sequence. The objective was to develop a treatment algorithm recommendation that is easier to interpret for the prescribing physician based on the available evidence and that is applicable in Europe and internationally. The knee was used as the model OA joint. METHODS: ESCEO assembled a task force of 13 international experts (rheumatologists, clinical epidemiologists, and clinical scientists). Existing guidelines were reviewed; all interventions listed and recent evidence were retrieved using established databases. A first schematic flow chart with treatment prioritization was discussed in a 1-day meeting and shaped to the treatment algorithm. Fine-tuning occurred by electronic communication and three consultation rounds until consensus. RESULTS: Basic principles consist of the need for a combined pharmacological and non-pharmacological treatment with a core set of initial measures, including information access/education, weight loss if overweight, and an appropriate exercise program. Four multimodal steps are then established. Step 1 consists of background therapy, either non-pharmacological (referral to a physical therapist for re-alignment treatment if needed and sequential introduction of further physical interventions initially and at any time thereafter) or pharmacological. The latter consists of chronic Symptomatic Slow-Acting Drugs for OA (e.g., prescription glucosamine sulfate and/or chondroitin sulfate) with paracetamol at-need; topical NSAIDs are added in the still symptomatic patient. Step 2 consists of the advanced pharmacological management in the persistent symptomatic patient and is centered on the use of oral COX-2 selective or non-selective NSAIDs, chosen based on concomitant risk factors, with intra-articular corticosteroids or hyaluronate for further symptom relief if insufficient. In Step 3, the last pharmacological attempts before surgery are represented by weak opioids and other central analgesics. Finally, Step 4 consists of end-stage disease management and surgery, with classical opioids as a difficult-to-manage alternative when surgery is contraindicated. CONCLUSIONS: The proposed treatment algorithm may represent a new framework for the development of future guidelines for the management of OA, more easily accessible to physicians.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.982
Threshold uncertainty score0.605

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.012
GPT teacher head0.271
Teacher spread0.259 · 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