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Functional outcome of proximal tibia intra-articular fractures after open reduction and internal fixation

2023· article· en· W4319229884 on OpenAlexaboutno aff
Nagesh Akhade, Ranjit Chopade, Pranav Shere, Pratik Shahare

Bibliographic record

VenueInternational Journal of Research in Orthopaedics · 2023
Typearticle
Languageen
FieldMedicine
TopicBone fractures and treatments
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineInternal fixationWOMACOsteoarthritisSurgeryTibia

Abstract

fetched live from OpenAlex

Background: Tibial plateau fractures are common fractures which constitute approximately 1% of all fractures. These involves the particular area of tibia and difficult to manage. These fractures require absolute stable fixation to improve the function and to prevent the post traumatic arthritis. We aim assess the functional outcome of patients in proximal tibia fractures treated with ORIF. Methods: Patients assessed at OPD visits with knee injury and Osteoarthritis score (KOOS) and Westerna Ontario and Mc-master university OA index (WOMAC). With informed consent patients are assessed one year after fixation of fractures and data recorded in case record forms. Results: Out of 74 patients studied, majority belonged to age group 21 to 40 years old (44 cases, 59.45%); followed by 21 cases (36.48%) from age group 41-60 years old. 7 patients (9.45%) were found in age groups 61 to 80 years old. Mean KOOS scores shows FAIR outcome in type I, type II, type III and type IV Schatzkers. While poor outcome was found in type V and type V and type VI. High energy trauma is associated with poor functional outcome as compared to low energy trauma. Mean WOMAC scores are found good in type I, type II fractures while fair outcome found in type III and type IV, poor outcome observed in type V and type VI Schatzkers type. Conclusions: As per this study we concluded that fractures treated with ORIF showed good functional outcome. ORIF with buttress plate gives good to fair results.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.028
Threshold uncertainty score0.311

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.078
GPT teacher head0.441
Teacher spread0.363 · 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 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
Published2023
Admission routes1
Has abstractyes

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