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Record W7104550668 · doi:10.48047/gkv5d222

Decoding The Diagnostic Triad: A 2018-2020 Study on the Clinical, MRI and Arthroscopic Correlation in Meniscal And ACL Injuries at BSMMU

2022· article· W7104550668 on OpenAlexaff

Bibliographic record

VenueCuestiones de Fisioterapia · 2022
Typearticle
Language
FieldMedicine
TopicKnee injuries and reconstruction techniques
Canadian institutionsBP (Canada)
Fundersnot available
KeywordsAnterior cruciate ligamentOsteoarthritisLigamentArthroscopyKnee JointPopulationCruciate ligamentMeniscus

Abstract

fetched live from OpenAlex

This prospective study aimed to evaluate the correlation between clinical and arthroscopic findings in knee injuries and compare them with MRI-based diagnoses. The research was conducted at BSMMU, Dhaka, between 2018 and 2020. The study population consisted of patients with knee injuries, specifically those with cruciate ligament and/or meniscal injuries that led to persistent knee instability for at least 1.5 months, remained unresponsive to conservative treatment, and did not have osteoarthritis or intra-articular fractures. Patients with a history of knee surgery, osteoarthritis, or ligament and meniscal injuries associated with intra-articular fractures were excluded. A total of 30 eligible cases were consecutively included in the study

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)0.003
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.024
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.001
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 head0.028
GPT teacher head0.340
Teacher spread0.312 · 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 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
Published2022
Admission routes1
Has abstractyes

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