Bibliographic record
Abstract
BACKGROUND: The most common cause of recurring lost time from work, low back pain is a huge burden on society. Medical training dictates that we must establish a cause for pain before we can treat it and then base our treatment on a recognized and agreed-upon pathology. But in the overwhelming majority of low back pain cases, the issue is nothing more than a minor mechanical malfunction, the inevitable consequence of normal wear and tear. The severity of the pain does not reflect the benign nature of the underlying problem and its limited extent makes a definitive diagnosis impossible. One important component of the solution is improved spinal triage. Using patterns or syndromes in the initial assessment of low back pain is gaining renewed interest and clinical acceptance. METHODS: Identifying a patient's pain pattern is achieved primarily through an assessment of the patient's history. The patient interview begins with a series of questions to determine the specific syndrome. A subsequent physical examination supports or refutes the findings in history. Combining information from the history with the findings of the physical examination, the clinician has the ability to rule out a number of potentially grim diagnoses. RESULTS: More than 90% of back pain patients have benign mechanical problems and their pain can be classified into 4 distinct patterns: 2 back-dominant patterns and 2 leg-dominant patterns. CONCLUSION: A clinical perspective capable of recognizing a defined syndrome at first contact will lead to a better outcome. Most patients with low back pain can be treated successfully with simple, pattern-specific, noninvasive primary management. Patients without a pattern and those who do not respond as anticipated require further investigation and specialized care.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".