Ultimate Back Fitness and Performance, Second Edition By Stuart McGill, Ph.D
Bibliographic record
Abstract
Evidence-based approaches are the forefront of making solid decisions in the treatment of patients in the clinical setting. While it is difficult to stay abreast of the latest trends in healthcare, evidence-based practicing takes into account clinical experiences by practitioners, the patient’s core values and expectations, and what the evidence of research suggests. As chiropractic care is becoming accepted for various clinical presentations, patients are seeking alternatives, and DC’s are being confronted with various clinical problems that they are not familiar with treating. This warrants an approach to effectively treat the patient, utilizing past effective clinical decisions, evidence in the form of research findings, and filtered information from the vast explosion of information offered via the internet. Combined with the patient’s specific presenting symptoms and values, one can utilize the EBC approach to alter what evidence suggests with the patient’s specific needs. This selection offers a solid introduction on the premise of EBC, and then a schematic review of the components involved in this practice approach. It offers a useful section on commonly encountered research designs, and a protocol for appraising each specific tactic. The concluding section proposes practical applications of implementing an evidence-based chiropractic practice, and numerous appendices to exploit. As emphasized, in order to make progress as an EBC practitioner, you must regularly implement the procedures outlined. The practitioner’s clinical expertise is paramount in the application of clinical evidence to patient circumstances, but it is up to the clinician to ultimately determine the best use of the information, particular to the presenting case. If not already shelved for utilization, Evidence-Based Chiropractic Practice is must-have for any practitioner’s reference library.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".