Naming the Soft Tissue Layers of the Temporoparietal Region
Bibliographic record
Abstract
BACKGROUND: The complexity of temporoparietal anatomy is compounded by inconsistent nomenclature. OBJECTIVE: To provide a comprehensive review of the variations in terminology and anatomic descriptions of the temporoparietal soft tissue layers, with the aim of improving learning and communication across surgical disciplines. METHODS: MEDLINE (1950-2009) searches were conducted for anatomic studies of the temporoparietal region, and for studies describing temporoparietal anatomy in the context of surgical techniques. RESULTS: Sixty-nine articles were included in the review. Naming of the soft tissue layers of the temporoparietal region was inconsistent both within and across surgical disciplines, with several terms utilized for the same layer and occasionally the same term applied to different layers. Studies also varied in their description of the vascular, neural, and soft tissue architecture of the temporoparietal region. CONCLUSION: A uniform, descriptive nomenclature is paramount to facilitating surgical education and interpreting future studies. A naming system based on the Terminologica Anatomica is proposed in this review. From superficial to deep, the proposed terms for the soft tissue layers of the temporoparietal region include: temporoparietal fascia, loose areolar tissue plane, superficial leaflet of temporal fascia, fat pad of temporal fascia, deep leaflet of temporal fascia, fat pad deep to temporal fascia, temporalis or temporal muscle, and pericranium.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".