To treat or not to treat: new evidence for the effectiveness of manual therapy
Bibliographic record
Abstract
Manual therapy has been shown to be effective for certain conditons but more research is needed to identify other suitable patients Recent randomised clinical trials found manual therapy to be more effective than other methods of conservative management for low back and neck pain.1–5 On the other hand, some randomised clinical trials,6–13 systematic reviews,14 and meta-analyses15 concluded that there was no evidence that spinal manipulative therapy is superior to other standard treatments for patients with low back or neck pain. This provides the clinician with a Shakespearean quandary—to treat or not to treat using manual therapies? Therefore this leader addresses the question: what explains these apparently inconsistent data?. The term manual therapy has many connotations, but for this leader it includes manually performed assessment and treatment methods (which can include joint, neural tissue, and/or muscle techniques). The term manipulation is typically used to describe small amplitude thrust techniques performed with speed.16 I searched Medline, Cinahl, and Embase databases for randomised clinical trials comparing spinal manual joint techniques (mobilisation with or without manipulation) or manipulation only with other conservative treatments for back or neck pain. Only studies published as full papers, in English, between 1 January 1998 and 31 December 2003 were included. Pilot studies were not included. Table 1 outlines search strategies for each database. Thirteen studies met the criteria (table 2). One study of bone setting by Finnish folk healers who lacked formal education17 was excluded as all other studies involved formally educated professionals. View this table: Table 1 Search strategy View this table: Table 2 Studies reviewed Examining the trials for homogeneity revealed that the mean age of participants was similar among the studies and most participants were white (except for two studies6,11). Thus factors related to the population studied did not appear to explain the conflicting results. …
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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 | 0.051 | 0.162 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.011 |
| Bibliometrics | 0.012 | 0.009 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.022 | 0.002 |
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".