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Record W4382468454 · doi:10.53730/ijhs.v7ns1.14409

Effect of triamcinolone acetonide in trigeminal neuralgia (TN) pain

2023· article· en· W4382468454 on OpenAlexaff
Kashif Adnan, Nayab Tariq, Sana Shakil Khan, Amir Abkar Shaikh, Yousuf Moosa, Aahmar Maqsood, Assad Ullah

Bibliographic record

VenueInternational Journal of Health Sciences · 2023
Typearticle
Languageen
FieldMedicine
TopicTrigeminal Neuralgia and Treatments
Canadian institutionsCollège Montmorency
Fundersnot available
KeywordsTriamcinolone acetonideMedicineTrigeminal neuralgiaAnesthesiaNeuralgiaTrigeminal nerveNeuropathic painSurgery

Abstract

fetched live from OpenAlex

Background: Trigeminal neuralgia is a form of neuropathic pain caused by trigeminal nerve. Anticonvulsants are primary class of pharmaceuticals used to treat pain in trigeminal neuralgia patients. Another conservative treatment option for controlling this pain includes blocking of nerve. Objectives: To determine how trigeminal neuralgia patients respond to triamcinolone acetonide. Methods: The department of oral and maxillofacial surgery at de'Montmorency College of Dentistry/Punjab dental hospital conducted a cross-sectional study over a six-month period. 35 study participants underwent a clinical examination along with a history-taking process. Written consent was obtained. After gathering the necessary information, the affected nerve was identified followed by the administration of local anesthesia. The most painful area was identified and 5 mL of bupivacaine and 40 mg of triamcinolone acetonide was administered at that side. After five minutes, the participant's level of pain was assessed, and they were contacted back for follow-up after seven days. Every item on the post-op list of targeted goals was checked off in the participant's questionnaire at the follow-up day. With SPSS version 24, the statistical analysis was completed. Results: There were 35 participants in the study overall, with the majority of them being in their 40s or 50s.

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)0.005
metaresearch head score (Gemma)0.000
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 score0.321
Threshold uncertainty score0.255

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.038
GPT teacher head0.428
Teacher spread0.389 · 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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