The accuracy of clinical kyphosis examination for detection of thoracic vertebral fractures: comparison of direct and indirect kyphosis measures.
Bibliographic record
Abstract
OBJECTIVE: To compare the accuracies of two simple physical examination maneuvers for detecting the presence of thoracic vertebral fractures (VF) diagnosed by radiography: direct measurement of kyphosis angle (KA, in degrees) and indirect measurement using wall-occiput distance (WOD, in cm). METHODS: Subjects were 280 women (average age, 54.5 years; range, 18-92) referred for assessment of osteoporosis. KA was measured from T4 to T12 using a digital inclinometer while WOD was measured with the patient in a standardized position. VF were diagnosed on radiographs using semi-quantitative morphometry. RESULTS: KA and WOD were moderately correlated (r = 0.72, p<10(-11)). KA increased by 3.7(o) (95% CI, 2.6-4.8(o)) for each VF (p = 4x 10(-11)) and WOD rose 1.3 cm (95% CI, 0.8-1.7 cm) per VF (p = 2 x 10(-11)). The areas under the receiver operating characteristic curves were 0.72 (95% CI, 0.65-0.79) for KA and 0.76 (95% CI, 0.69-0.82) for WOD, which were not significantly different (p = 0.13). CONCLUSIONS: Given similar performances of direct and indirect measures of kyphosis, we propose that WOD should be used in clinical practice, with a clinical threshold of WOD>4.0 cm as an indication to consider spine radiography. At this WOD threshold, sensitivity was 41% (95% CI, 31-52%) and specificity was 92% (95% CI, 87-95%). WOD should be considered for use in the clinical assessment of osteoporosis patients.
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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 | 0.003 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".