Inpatient Acupuncture at a Major Cancer Center
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 it