Children and spinal manipulation therapy: Ask your patients about all the therapies they seek
Bibliographic record
Abstract
Two days before his school hockey league finals, a previously healthy eight-year-old boy had a runny nose and low-grade fever. He performed well during the first game with two goals. Afterwards, he complained of difficulties in moving his neck because of pain. To prepare him for the next game, his grandfather, who previously had significant improvement of his back pain with spinal manipulation, booked an appointment with a chiropractor. The next morning, the boy felt exhausted and was a little sleepier than usual. The chiropractor found his cervical spine difficult to mobilize and recommended a return visit the next day for follow-up. Twelve hours later, the child arrived at the emergency department by ambulance after a generalized tonic-clonic seizure lasting less than 2 min. The initial assessment revealed a Glasgow coma scale of nine, with a rigid neck and normal pupils. His blood sugar and electrolyte levels were normal, and a computed tomography scan of his head was unremarkable. Because of persisting symptoms, a lumbar puncture was performed. An examination of the cerebrospinal fluid showed a white blood cell count of 7 × 106/L with 75% lymphocytes, a low glucose and a protein level of 0.63 g/L. A diagnosis of viral meningitis was confirmed by polymerase chain reaction. After a few days in the hospital, he improved and was discharged home.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".