Bibliographic record
Abstract
STUDY DESIGN: Positive pain responses provoked in an inclusive series of cervical discograms performed over a 12-year period were categorized by level and reviewed. OBJECTIVES: To report the prevalence of cervical pathology over an entire series of patients, to determine whether a reproducible pattern of concordant pain could be associated with each symptomatic level identified, and to calculate the rate of complications. SUMMARY OF BACKGROUND DATA: Cloward wrote the first articles explaining the technique of cervical discography and reported on the pain responses induced. Currently, the technique is viewed as an invaluable diagnostic tool, but it also is criticized for failing to contribute unique information beyond that available from imaging studies despite the inherent risks. METHODS: A series of 173 cervical discograms performed over 12 years was examined. Pain responses provoked and recorded during discography were grouped by disc level and examined for recurring patterns. The prevalence of disc pathology was calculated. RESULTS: In all, 807 discs were injected, and 404 concordant pain responses (50%) were elicited. Three or more abnormal disc levels were identified in more than half of the patients. Complications developed in four patients (2.3%). No further complications were reported. Surgical treatment was indicated as viable in only 35 studies. CONCLUSIONS: Discography is a safe and valuable diagnostic procedure showing characteristic pain patterns that may have clinical significance. In more than half of the studies, three or more levels were identified as pain generators, suggesting that treatment decisions based on information from fewer discs injected during discography may be tenuous.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.013 | 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".