Bibliographic record
Abstract
Abstract Low back pain (LBP) is the commonest cause of disability in persons under 46 years of age.1 LBP remains one of the most difficult challenges that face health care providers, as there are 9 important factors that compound the ability to manage, minimize the cost and predict its outcome: Low back pain is a symptom and not a disease yet it remains the commonest used term to describe the diagnosis.2Low back pain can be caused due to injury or disease process.2There are many diseases in systems other than musculo-skeletal systems that cause LBP.2Most types of back pain closely mimic each other.2Part of the problem is due to the fact that the region of the low back is extremely complex, both anatomically and functionally.3Another difficulty is the inability to objectively define a measurement for pain. 3A large part of the problem is the cultural need to medicalize LBP, linking the symptoms to radiological abnormalities requested by physicians. 2Physicians' insufficient understanding of the topic and its management leading to patient advocacy.2,3Poor medical results of occupationally related LBP in institutions unprepared to deal with this problem.2,3 Many psychological factors are thought to be causative in development, exacerbation and/or maintenance of chronic LBP.4
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.005 | 0.002 |
| Insufficient payload (model declined to judge) | 0.055 | 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".