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Record W7053445373

Valor diagnóstico de las reglas de ottawa en las lesiones agudas de tobillo o pie en el hospital virgen de Fátima de Chachapoyas

2020· dissertation· es· W7053445373 on OpenAlexaboutno aff

Bibliographic record

VenueALICIA National Digital Repository (Antenor Orrego Private University) · 2020
Typedissertation
Languagees
FieldEngineering
TopicMagneto-Optical Properties and Applications
Canadian institutionsnot available
Fundersnot available
KeywordsUniversity hospitalCongenital malformationsRegional hospital
DOInot available

Abstract

fetched live from OpenAlex

Las lesiones agudas de tobillo son una de las afecciones ortopédicas más comunes, estimadas en alrededor del 15% al 25% de todas las lesiones que ocurren en las unidades de atención de emergencia. La necesidad de solicitar pruebas adicionales para esguinces de tobillo se basa principalmente en la sospecha y la exclusión del diagnóstico de fracturas. Alrededor del 85% de las radiografías tomadas en pacientes con lesiones agudas de tobillo son normales. Las reglas de Ottawa para el tobillo establecen criterios para solicitar radiografías evitando exámenes innecesarios. El objetivo de este trabajo es determinar el valor diagnóstico de las reglas de Ottawa para el diagnóstico de las lesiones agudas de tobillo en el servicio de emergencia del Hospital Regional Virgen de Fátima de Chachapoyas durante los años 2020 y 2021. Para lo cual se realizará un estudio observacional prospectivo, transversal, basados en 521 pacientes, en quienes evaluaremos clínicamente con las reglas de Ottawa a todos los pacientes que lleguen al servicio de emergencia y correlacionaremos con las imágenes radiológicas de tobillo o pie.

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)0.002
metaresearch head score (Gemma)0.010
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.211
Threshold uncertainty score0.419

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.010
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.005
GPT teacher head0.203
Teacher spread0.198 · 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

Citations0
Published2020
Admission routes1
Has abstractyes

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