The Relationship Between Salivary Alpha Amylase Activity and Score of McGill Pain Questionnaire in Patients With Tension Type Headache
Bibliographic record
Abstract
INTRODUCTION: Tension-type headache is the most common type of headache across the world. Saliva as a non-invasive medium is used to detect a wide range of diseases. Salivary Alpha-Amylase (SAA) levels has been suggested as a potential indirect marker for detecting Sympathoadrenal Medullary (SAM) activity, which is activated by pain. Significant correlation was found between SAA levels and pain scale in patients with chronic pain. The purpose of the present study was to measure SAA activity in Frequent Episodic Tension-Type Headache (FETTH). In addition to the Visual Analogue Scale (VAS), we intend to assess intensity and various aspects of pain by McGill Pain Questionnaire (MPQ). METHODS: A total of 45 females with FETTH (case group) and 45 healthy voluntary females (control group) were enrolled in our case-control study. Unstimulated saliva by spitting method was taken from each participant. RESULTS: SAA levels were significantly higher in patients with FETTH (P<0.001) when compared with the control group. There was significant correlation between SAA activity and MPQ score (P<0.001). CONCLUSION: This is the first study using MPQ as a subjective means of assessing quality and quantity of pain alongside the VAS as an objective tool for evaluating pain in patients with FETTH. SAA may be an appropriate marker for assessing of pain levels in patients with FETTH. MPQ versus the VAS may be a more accurate measurement tools along VAS.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".