Traditional Arabic and Islamic Medicine Primary Methods in Applied Therapy
Bibliographic record
Abstract
Applied therapy is a commonly utilized method of treatment for preventive and therapeutic measures. Avicenna, a significant physician of the Islamic golden age, described 36 methods to restore balance of patients’ elements, humors and faculties. We propose a categorization of these methods within a single theory and framework, as this has previously been lacking. To be considered under the rubric of TAIM applied therapies, the procedures must have: 1) proof of use in the Arab and Muslim world; 2) considered an essential component of Avicenna’s compendium of regimental therapy; and 3) historical lineage according to regional, cultural or Islamic healing practices. We developed a taxonomy of applied therapies by denoting each as a primary or supportive method and providing a definition for each category of methods. We define applied therapy as techniques or procedures involving physical and manual contact with the individual that are aimed at restoring health and preventing illness. Primary methods describe therapies which when used individually can impact the vital force of the body in order to preserve or restore health, while supportive methods describe therapies used in conjunction with primary methods intended to augment or create a synergistic and enhanced effect, exceeding that of primary methods alone. Our work provides a fundamental step in continuing the evolution of the TAIM conceptual model and advancing our understanding of the diverse practices under the rubric of applied therapy. Researchers can use this comprehensive TAIM taxonomy for investigating the respective elements, and systematically exploring the theoretical and therapeutic applications.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".