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Record W2767602212 · doi:10.18535/jmscr/v5i11.38

Is Intraarticular Administration of Tranexamic Acid Better than Its Intravenous Administration in Reducing Blood loss after total Knee Arthroplasty?

2017· article· en· W2767602212 on OpenAlexaboutno aff
Dr Noorul Ameen

Bibliographic record

VenueJournal of Medical Science And clinical Research · 2017
Typearticle
Languageen
FieldMedicine
TopicBlood transfusion and management
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineTranexamic acidBlood lossTotal knee arthroplastyAdministration (probate law)AnesthesiaSurgeryArthroplasty

Abstract

fetched live from OpenAlex

Context: Intravenous (IV) tranexamic acid (TXA) is a goodpotent agent in controlling postoperative blood loss following total knee arthroplasty (TKA). Recently, intraarticular usage of this agent has also shown good results. Aims: Comparison of postoperative blood loss between IV(intravenous) and topical administration of TXA tranexamic acid in TKAs(total knee arthroplasty) Materials and Design: Eighty-six TKAs on knees were included in ourstudy. Randomization was done, so that in our study 40 TKA received 1 g of IV TXA, while 46 had underwent intraarticular administration of 1 gm of TXA. Subjects and Methods: We have compared the postoperative blood loss by calculating the difference in pre-and postop hemoglobin and the need for blood transfusion for patients .The functional assessment was done on basis of Western Ontario McMaster Osteo-Arthritis Index (WOMAC) scores and complications like postoperative infection, oozing from wound site and thromboembolic manifestations. Results: The blood loss was significantly less in the intraarticular administration group as compared to the IV injection group. The total blood loss, blood transfusion, and drain output was also less but the difference was not significant. The functional assessment (WOMAC) scores were equivocal and so were the complications including thromboembolic manifestations (

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)0.016
metaresearch head score (Gemma)0.007
Version: codex-gemma-dda1882f352aValidation 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 score0.807
Threshold uncertainty score0.816

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0160.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
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 head0.087
GPT teacher head0.455
Teacher spread0.368 · 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
Published2017
Admission routes1
Has abstractyes

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