Bibliographic record
Abstract
A previous special issue of JOSPT (October 2016) discussed whiplash in terms of the clinical problems and current research surrounding prevention, biomechanics of injury, emergent care, imaging advancements, recovery pathways and prognosis, pathogenesis of posttrauma pain, acute and chronic management, and new predictive clinical tools. While great strides have been made in the field of whiplash and are continuing in earnest, a key group of clinicians and academics have recognized that inconsistent outcomes in published literature hamper our ability to meaningfully synthesize research findings, leading to results of systematic reviews that provide very few concrete clinical recommendations. We are optimistic that improved outcomes for people with whiplash-associated disorder (WAD) are attainable in the near future, as interdisciplinary research efforts continue to align internationally, new mechanisms are identified and explored, and advanced statistical techniques allow complex questions to be answered in clinically meaningful ways. J Orthop Sports Phys Ther 2017;47(7):444-446. doi:10.2519/jospt.2017.0106.
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 | 0.006 | 0.032 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.012 | 0.021 |
| Insufficient payload (model declined to judge) | 0.005 | 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".