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Record W3107728909

Use of Preoperative Submucosal Dexamethasone in Third Molar Surgery: A Step towards Improvement in Quality of Life.

2021· article· en· W3107728909 on OpenAlexaff
Samia Shad, Akif Mahmud, Adil Shahnawaz, Syed Majid Hussain Shah, Amber Farooq, Saqib Mehmood Khan, Rabia Rahat Gillani

Bibliographic record

VenuePubMed · 2021
Typearticle
Languageen
FieldDentistry
TopicDental Radiography and Imaging
Canadian institutionsAbbott (Canada)
Fundersnot available
KeywordsMedicineTrismusDexamethasoneSurgeryPlaceboMolarOral and maxillofacial surgeryRandomized controlled trialAnesthesiaQuality of life (healthcare)EdemaDentistryInternal medicine
DOInot available

Abstract

fetched live from OpenAlex

BACKGROUND: Surgical removal of impacted mandibular third molaris one of the most common procedures performed by Oral surgeons globally. The objective of the study was to ascertain theefficacyof pre-operative administration of submucosal dexamethasone on post-operative sequelae insurgically extracted impactedmandibular third molar.It was adouble-blind randomized controlled clinical trialthat wasperformed inthe Department of Oral & Maxillofacial Surgery at Abbottabad International Dental Hospital, Abbottabad from March 2019 to March 2020. METHODS: A total of 150 patientsweredivided into two groups,each having 75 patients. Group A received a placebo after administrationof local anesthesia whereas,group B received 4mg submucosaldexamethasone. A post-operative visit was scheduled after 48 hours to evaluate pain, facial swelling,and Trismus. RESULTS: On the second postoperative day, the patients in the experimental group presented with significantly reduced pain, facial swelling,and trismus in comparison to the control group. CONCLUSION: Pre-operativeadministration of 4mg dexamethasone through the submucosal route is efficacious inthe reduction of post-operative pain, swelling,and trismus in mandibular third molarsurgerythus enabling the patient to return to daily life activities earlier.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.046
Threshold uncertainty score0.614

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.066
GPT teacher head0.284
Teacher spread0.218 · 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

Citations3
Published2021
Admission routes1
Has abstractyes

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