Bibliographic record
Abstract
Every physician who treats injured patients has a responsibility to detect and appropriately manage thoracolumbar spinal column injuries. Fractures of the thoracolumbar spine are relatively common, so clinicians must give them every consideration both to protect from secondary spinal cord injury and to appreciate the extent of the patient's injuries. Other extraspinal as well as noncontiguous injuries to the spinal column are frequently present. Unfortunately, thoracolumbar spine fractures are often missed or diagnosed late in clinical series. In an era of cost-containment, not all responsive patients require full thoracolumbar spine radiographs. In awake, alert, nonintoxicated patients with simple injury mechanisms, these fractures can be ruled out through physical examination, if the patient has no physical findings and does not have other serious injuries. However, concern has recently been raised that some patients may have "asymptomatic" fractures that may be missed without radiography. The evidence reveals that fractures are not truly asymptomatic but may be masked by other distracting injuries, making them fractures occult rather than asymptomatic. Clinicians and subsequently their patients will always be at risk if this important distinction is forgotten.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.008 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.013 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 0.005 |
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".