Bibliographic record
Abstract
From the launch of any discussion about complementary and alternative medicine (CAM), we are challenged by the lack of clear definition. CAM has been defined as a group of diverse medical and health care systems, practices, and products that are not generally considered part of conventional medicine.1 ,p1 Although this is an example of one of the best working definitions of treatments considered CAM or integrative medicine, there are important limitations to the definition. First, the definition lumps together very different agents or interventions, and it is not possible to generalize about the safety and efficacy of these treatments. Second, the definition relies greatly on subjectivity; what is considered conventional or mainstream varies between health care providers and among patients. Third, the definition focuses on what CAM is not, mainly not inside the realm of mainstream treatment, rather than what CAM treatments actually are. Therefore, it was daunting mission for my colleagues and me on the American Psychiatric Association's Task Force on CAM in psychiatry to select topics on which to focus, as producing report on CAM in psychiatry was an elusive goal. We chose to focus on selected CAM treatments for major depressive disorder (MDD), and primarily reviewed treatments with enough efficacy and safety data, as well as common use in depression, to produce clinically relevant report.2 We sought evidence that is highly valued in Western medicine, data from randomized controlled trials (RCTs). It has been argued that some treatments considered CAM do not lend themselves easily to such study. For example, acupuncture and exercise are treatments in which finding appropriate control conditions is challenging. Also, some treatments are considered to be established treatments in particular contexts or cultures. However, we sought to assess an evidence base for these treatments with the same scrutiny we would apply to pharmacologic or psychotherapeutic intervention. Patients often use CAM treatments for psychiatric indications, and it has been documented that they often do not disclose the use of these treatments to their psychiatrists.3 Epidemiologic studies demonstrate high use of CAM, with up to 40% of adults in the United States using at least one treatment annually, with over $33 billion dollars spent out of pocket each year.3 CAM treatments may be particularly attractive to patients with psychiatric disorders. Many can be obtained without prescription or evaluation, in field of medicine (psychiatry) in which disorders are often highly stigmatized. However, this availability also carries substantial risk. Lack of appropriate diagnosis, evaluation, and monitoring is seriously risky in the course of disorders that are often severe and have high rates of morbidity and even mortality. Also, psychotropics often carry high burdens of side effects and risks, and the idea of CAM or natural treatment may carry connotations of safety, when safety may not be established. Even in the case of CAM treatments with benign side effect profiles, deferring effective treatment may result in trials of interventions that lack efficacy and have not received adequate study. In this issue, Dr David Mischoulon and colleagues (see Wu et al4) assess the data pertaining to the role of acupuncture in the treatment of depressive disorders. They systematically reviewed the literature for studies assessing the efficacy of acupuncture as monotherapy for depression, as an augmentation agent and as means by which to alleviate side effects of medication management of depression. The context and application of acupuncture historically has been different than that of drug development, as it has evolved in diagnostic systems that are different than Western medicine, and acupuncture is often used in parallel with other treatments in Eastern medicine. Wu et al4 include in their paper focus on populations that may particularly benefit from nonmedication treatment options for MDD, including pregnant women. …
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".