Clinicians’ Ability to Detect a Palpable Difference in Spinal Stiffness Compared With a Mechanical Device
Bibliographic record
Abstract
OBJECTIVE: The purpose of this study was to quantify the threshold at which clinicians can detect a difference in spinal stiffness of the thoracic and lumbar spine via palpation and then determine if this detection threshold would affect a clinician's ability to identify changes in spinal stiffness as measured by an objective instrument. METHODS: In this study, the threshold at which a change in spinal stiffness was detected was quantified in 12 experienced clinicians (physical therapists and doctors of chiropractic) by changing the differential stiffness in 2 inflatable targets until the clinician could no longer identify which was stiffer. In the second part of the study, clinicians then were asked to palpate pre-identified pairs of vertebrae in an asymptomatic volunteer and to identify the stiffer of the pair (T7 and L3, T7 and L4, L3 and L4), and the biomechanical stiffness of each vertebral pair was quantified objectively by a validated instrument. RESULTS: The mean stiffness detection threshold for the clinicians was 8%. Objective measurement of the stiffness differential between vertebral pairs was 30% for T7* and L3, 20% for T7* and L4, and 10% for L3* and L4 (*denotes the stiffer of the pair). Ten of 12 clinicians correctly identified T7 as stiffer when compared with L3 and T7 as stiffer than L4. Alternatively, when the differential vertebral pair stiffness was similar to the stiffness detection threshold (~8%), clinicians were less successful in identifying the stiffer vertebra of the pair; 4 of 12 clinicians correctly identified L3 as being stiffer compared with L4. CONCLUSION: These results suggest that the physiological limits of human palpation may limit the ability of clinicians to identify small alterations in spine stiffness.
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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 | 0.009 | 0.089 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".