Case series: Noninfectious myositis of temporal muscle: a report of 2 cases
Bibliographic record
Abstract
BACKGROUND: Noninfectious myositis (NIM) of the masticatory muscles is uncommon local myalgia disorder persisted by a centrally-mediated neurogenic mechanism. Due to the rarity of this condition and the lack of appropriate data regarding it, diagnosing this pathology when it affects the temporal muscle (TM) is challenging. CASE PRESENTATION: Clinical signs and symptoms, diagnostic process, and treatment outcome of 2 rare cases of NIM of the TM were presented. The signs and symptoms of the patients were not pathognomonic. There were restrictions on the mouth opening and lateral excursion of the mandible. The duration of the symptoms may not be chronic. The findings of clinical evaluation may indicate the diagnosis of anterior disc displacement (DD) without reduction of the temporomandibular joint (TMJ) and/or local myalgia. Swelling of the involved muscle could be evident and identified on palpation depending on the involved site of myositis. The axial T2-weighted magnetic resonance (MR) imaging was important for the accurate diagnosis of this rare condition. Application of non-surgical conservative treatment modalities such as administration of non-steroidal anti-inflammatory analgesics for a sufficient period of time, control of oral parafunctional habits, and jaw exercises were effective for the management of NIM of the TM. CONCLUSION: A thorough clinical examination and MR imaging including the axial T2-weighted view are required for accurate diagnosis and effective management of NIM of the TM.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".