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Record W2009861745 · doi:10.1016/s0212-6567(14)70042-3

Evaluación del paciente y medida de resultados

2014· article· es· W2009861745 on OpenAlexaboutno aff
Enrique Nieto Pol

Bibliographic record

VenueAtención Primaria · 2014
Typearticle
Languagees
FieldMedicine
TopicOsteoarthritis Treatment and Mechanisms
Canadian institutionsnot available
Fundersnot available
KeywordsWOMACOsteoarthritisPhysical therapyMedicineQuality of life (healthcare)Clinical PracticeVisual analogue scalePsychological interventionPhysical medicine and rehabilitationAlternative medicine

Abstract

fetched live from OpenAlex

Tanto la valoración inicial como el seguimiento del paciente con artrosis precisan de una evaluación sistematizada de indicadores que informen sobre el grado de afectación de esta y permitan cuantificarla. Objetivar la evolución de la enfermedad facilita la toma de decisiones del clínico y proporciona información válida sobre la respuesta y eficacia de las distintas intervenciones terapéuticas. Los instrumentos recomendados en investigación, como medidas de resultado de la artrosis, son la valoración del dolor, de la capacidad funcional y la evaluación global por parte del enfermo. En estudios de más de 1 año de duración se valoran cambios estructurales mediante radiología simple. También se recomienda, de manera opcional, la valoración de la calidad de vida del paciente y la evaluación global de la enfermedad por parte del médico. Estos indicadores deberían incorporarse a la práctica clínica habitual para una adecuada valoración y un correcto seguimiento del paciente con artrosis. El método de evaluación del dolor recomendado para su uso en la práctica clínica es la escala visual analógica (EVA). El mejor instrumento para la valoración de la capacidad funcional del paciente con artrosis de cadera o rodilla es la escala WOMAC (Western Ontario and McMaster Universities Osteoarthritis Index). Para la evaluación global de la enfermedad por parte del paciente con artrosis, en la práctica habitual, se recomienda el uso de la EVA o el cuestionario de salud SF-12 (12-item short-form health survey). Both the initial evaluation and follow-up of patients with osteoarthritis require systematic evaluation of the indicators that provide information on the degree of involvement of the disease and allow its quantification. Reliable measures of disease progression help decision-making by clinicians and provide valid information on treatment response and the effectiveness of the distinct therapeutic interventions. The instruments recommended in research, as outcome measures in osteoarthritis, are pain evaluation, assessment of physical function, and self-reported global evaluation. In studies lasting more than 1 year, structural changes are evaluated through simple X-ray. Self-reported quality of life assessment and physician global assessment are also recommended as options. These indicators should be incorporated into routine clinical practice for adequate evaluation and correct follow-up of patients with osteoarthritis. The recommended pain evaluation method for use in clinical practice is the visual analog scale (VAS). The best instrument to evaluate physical function in patients with hip or knee osteoarthritis is the WOMAC scale (Western Ontario and McMaster Universities Osteoarthritis Index). For patient-reported global assessment in routine practice, the recommended scales are VAS or the SF-12 (12-item short-form health survey).

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.014
metaresearch head score (Gemma)0.040
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.014
Threshold uncertainty score0.074

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.040
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.001

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 head0.013
GPT teacher head0.282
Teacher spread0.269 · 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 source (direct Gemma or distilled Codex), 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

Citations4
Published2014
Admission routes1
Has abstractyes

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