Bibliographic record
Abstract
OBJECTIVE: The purpose of this paper is to elucidate this little known cause of upper back pain through a narrative review of the literature and to discuss the possible role of the dorsal scapular nerve (DSN) in the etiopathology of other similar diagnoses in this area including cervicogenic dorsalgia (CD), notalgia paresthetica (NP), SICK scapula and a posterolateral arm pain pattern. BACKGROUND: Dorsal scapular nerve (DSN) neuropathy has been a rarely thought of differential diagnosis for mid scapular, upper to mid back and costovertebral pain. These are common conditions presenting to chiropractic, physiotherapy, massage therapy and medical offices. METHODS: The methods used to gather articles for this paper included: searching electronic databases; and hand searching relevant references from journal articles and textbook chapters. RESULTS: One hundred-fourteen articles were retrieved. After removing duplicates, there were 57 articles of which 29 were retrieved. There were 26 articles and textbook chapters retrieved by hand searching equaling 55 articles retrieved of which 47 relevant articles were used in this report. DISCUSSION: The anatomy, pathway and function of the dorsal scapular nerve can be varied and exceptionally rarely may include a sensory component. The signs and symptoms, therefore, may include pain, atrophy, scapular winging, and dysesthesia. The mechanism of injury to the DSN is also quite varied ranging from postural to overuse in overhead work and sport. Other conditions in this area, including CD, NP, SICK scapula and a posterolateral arm pain pattern bear a striking resemblance to DSN neuropathy. CONCLUSION: DSN neuropathy should be included in the list of common differential diagnoses of upper and mid-thoracic pain, stiffness, dysesthesia and dysfunction. The study also brings forward interesting connections between DSN neuropathy, CD, NP, SICK scapula and a posterolateral arm pain pattern.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".