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Record W4303969026 · doi:10.36552/pjns.v26i3.772

Cerebellopontine Angle (CPA) Tumors Presenting with Trigeminal Neuralgia (TN): A Study from LRH, Peshawar

2022· article· en· W4303969026 on OpenAlexaff
Farooq Azam, Hamayun Tahir, Adnan Khaliq

Bibliographic record

VenuePakistan Journal Of Neurological Surgery · 2022
Typearticle
Languageen
FieldMedicine
TopicGlioma Diagnosis and Treatment
Canadian institutionsSmiths Detection (Canada)
Fundersnot available
KeywordsCerebellopontine angleTrigeminal neuralgiaMedicineTrigeminal nerveNeurosurgeryNeuralgiaMagnetic resonance imagingRadiologySurgeryAnesthesiaNeuropathic pain

Abstract

fetched live from OpenAlex

Background/Objective: The cerebellopontine angle (CPA) is the most prevalent site for brain tumors, accounting for 10% of all cases. CPA tumors can have a direct or indirect pathogenic impact on the auditory nerve and brain stem. The study aimed to quantify the prevalence of cerebellopontine angle tumors in patients with trigeminal neuralgia. Material and Methods: A cross sections study was conducted and 100 patients were included from the Neurosurgery department of LRH, Peshawar. Magnetic resonance images (MRI) were used to look for CPA tumors. The data on CPA tumors were stratified for age and gender. Suboccipital retromastoid craniectomy was performed. Results: The mean age of the patients was 43 years. 38 patients were male and 62 were female. CPA tumors were seen in three percent of trigeminal neuralgia patients. There existed a significant difference (p < 0.00001) between the presence and absence of CPA tumors. A maximum number of patients (n = 37) were not having CPA tumors from the age group of 51-60 years. An insignificant association was reported for CPA distribution concerning age and gender. Conclusion: According to our findings, 3% of trigeminal neuralgia patients had cerebellopontine angle tumors. We urge more investigation and screening of trigeminal neuralgia patients for CPA tumors based on the findings of this 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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.052
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.001
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 head0.029
GPT teacher head0.277
Teacher spread0.248 · 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

Citations1
Published2022
Admission routes1
Has abstractyes

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