Can Tibial Tubercle Trochlear Groove <TT-TG> Distance be Measured Clinically?
Bibliographic record
Abstract
Objectives: To compare the results of clinical TT-TG measurement to CT measurement. Methods: Eighteen patients were enrolled in the study. Twenty clinical and twenty CT TT-TG measurements were obtained as two patients had bilateral patella instability. Clinical TT-TG measurement method Three fellowship-trained orthopaedic surgeons used the following standardized technique: with the patient sitting on the examination table with knee flexed at 90 degrees over the edge of the table. The initial step is to mark surface anatomy. The inferior pole of the patella is identified and marked. A plumb line from the inferior patella pole is then extended distally to the horizontal level of the tibial tubercle. A level is used to ensure the plumb line is collinear all the way distally starting from the inferior patella pole. The horizontal distance between the plumb line and tibial tubercle is then measured. CT measurement methodTwo fellowship-trained MSK radiologists used the following standardized technique: the patient position is supine, feet first with the knees flexed 40 degrees on a bolster. The ankles are strapped together with a cushion of fixed size in between and both ankles are secured to the table top to prevent motion. Scans are taken from proximal to the patella to distal to the tibial tubercle. Axial images are used to identify the deepest portion of the trochlear groove and apex of the tibial tubercle. These two images are then superimposed on each other and the horizontal TT-TG distance between these two anatomic landmarks is measured. Statistical method: A Bland-Altman analysis comparing the agreement between clinical and CT TT-TG measurement was used. Three Steps: A one-sample t-test was used to assess the presence of fixed bias. This assesses whether the clinical measurement resulted in consistently higher or lower values than the CT measurements. A Bland-Altman plot with 95% limits of agreement was used to determine if the clinical and CT methods could be used interchangeably. The plot consists of the differences between each patient’s clinical and CT measurements on the Y axis versus the average of the measurements on the X axis. A regression analysis was performed to determine whether the differences between clinical and CT measurements were associated with the magnitude of the actual TT-TG distance. Results: 1. 1. The fixed bias value was -2.075 mm which indicated that on average, CT TT-TG distance was greater by 2.075 mm compared with clinical measurement. However, the result of the one sample t-test (p=0.06) was not statistically significant, providing no evidence of a fixed bias at the traditional 5% two-sided level. 2. Bland-Altman Plot The upper and lower limits of the differences between the two methods were 9.155 mm and -14.565 mm. This suggests that 95% of the time, differences between clinical and CT measurements will fall between these limits. The plot also indicated that it is feasible for clinical measurements to overestimate TT-TG distance by as much as 9.155 mm and to underestimate the difference by as much as 14.565 mm. 3. The linear regression analysis (p = 0.43) did not indicate that the magnitude of TT-TG distance played a role in inter-method agreement. Conclusion: Clinical measurement can be used to estimate TT-TG distance but clinicians should be aware of its limitations. Clinical TT-TG measurement can both overestimate and underestimate the value when compared to CT scan. Clinical measurement cannot replace CT measurement.
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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 | 0.008 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".