Inpatient Acupuncture at a Major Cancer Center
Bibliographic record
Abstract
BACKGROUND: Use of complementary and integrative therapies is increasing among cancer patients, but data regarding the impact treatments such as acupuncture have in an inpatient oncology setting are limited. METHODS: Patients who received acupuncture in an inpatient hospital environment between December 2014 and December 2015 were asked to complete a modified Edmonton Symptom Assessment Scale (ESAS; 0-10 scale) before and after each visit. Pre- and post-treatment scores were examined for each symptom using paired t tests. RESULTS: A total of 172 inpatients were treated with acupuncture in their hospital beds (257 visits). Thirty percent (n = 51) received at least one additional follow-up treatment (mean visits/patient = 1.5). Completion rate of the modified ESAS after acupuncture was 42%. The most common reasons for not completing the post-treatment ESAS were "patient too drowsy" or "patient fell asleep" (72%). For patients who reported a baseline symptom score ≥1, the greatest improvements (mean change ± SD) after acupuncture on the initial visit were found for pain (-1.8 ± 2.2; n = 69; P < .0001), nausea (-1.2 ± 1.9; n = 30; P < .001), anxiety (-0.8 ± 1.8; n = 36; P = .01), drowsiness (-0.6 ± 1.8; n = 57; P = .02), and fatigue (-0.4 ± 1.1; n = 67; P = .008). For patients who received at least one follow-up visit, significant improvement from baseline was found for sleep disturbance (-2.5 ± 4.4; n = 17; P = .03), anxiety (-2.4 ± 1.7; n = 9; P = .002), pain (-2.3 ± 2.7; n = 20; P = .001), and drowsiness (-2.0 ± 2.6; n = 16; P = .008). CONCLUSIONS: Patients who received inpatient acupuncture at a major cancer center experienced significant improvement after treatment for pain, sleep disturbance, anxiety, drowsiness, nausea, and fatigue.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.030 | 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".