User’s guide to the chiropractic literature-IA: how to use an article about therapy
Bibliographic record
Abstract
Evidence-based practice is now a fundamental movement of established importance within health care, yet it remains a contentious concept for some chiropractors. Traditionally, chiropractic institutions have emphasized the development of clinical acumen over critical appraisal skills, 1 Keating J.C. Green B.N. Johnson C.D. “Research” and “science” in the first half of the chiropractic century. J Maniulative Physiol Ther. 1995; 18: 357-378 Google Scholar with the result that many practitioners may feel unequipped to interpret, or apply, the results of current literature to their everyday practices. Additionally, it can be difficult to incorporate the results of widely publicized studies that report equivocal or negative outcomes of modalities typically associated with chiropractic care (ie, manipulation) and which seem to fly in the face of positive clinical experience. Evidence-based practice is demanding a paradigm shift among all health care providers, and these concerns are not unique to chiropractic. CorrectionsJournal of Manipulative & Physiological TherapeuticsVol. 26Issue 6Preview Full-Text PDF
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 | 0.005 | 0.056 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.018 | 0.016 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.427 | 0.371 |
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".