HOW DEEP DO YOU GO? CLINICAL PREDICTION OF NASOPHARYNGEAL DEPTH BASED ON FACIAL MEASUREMENTS
Bibliographic record
Abstract
Objectives: Nasopharyngeal (NP) depth prediction is clinically relevant in performing medical procedures, and enhancing technique precision and safety for patients. Nonetheless, clinical predictive variables and normative data in adults remain limited. This study aimed to determine normative data on NP depth and its correlation to external facial measurements. Methods: A multicenter cross-sectional study obtained data from adults presenting to otolaryngology clinics at five sites in Canada, Italy, and Spain. Investigators compared the endoscopically measured depth from sill to nasopharynx along the nasal floor to the facial measurements “curved distance from the alar-facial groove along the face to the tragus” and “distance from the tragus to a plane perpendicular to the philtrum.” When sinus CT images were available, the distance from the nasopharynx to nasal sill was also collected. Results: 371 patients participated in the study (41% women; 51 years old, SD 18). The average endoscopic depth was 9.4 cm (SD 0.86) and 10.1 cm (SD 0.9) for women and men, respectively (p<0.001; 95% CI 0.46 to 0.86). Perpendicular distance was strongly correlated to NP depth (r=0.775; p<0.001), with an average underestimation of 0.1 cm (SD 0.65; 95% CI 0.06 to 0.2). The equation: ND(cm) = perpendicular distance*0.773 + 2.344, generated from 271 randomly selected participants, and validated on 100 participants, resulted in a 0.03 cm prediction error (SD 0.61; 95% CI -0.08 to 0.16). Conclusions: Nasopharyngeal depth can be accurately approximated by the distance from the tragus to a plane perpendicular to philtrum. The generated predictive equation was most accurate but not likely clinically relevant.
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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 | 0.006 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".