Efficacy of hyperbaric oxygen combined with escitalopram in depression and its effect on cognitive function
Bibliographic record
Abstract
Objective: To investigate the efficacy of hyperbaric oxygen (HBO) combined with escitalopram in patients with depression and its effect on cognitive function. Methods: From 2016 to 2018, seventy patients with depression aged 18-65 years treated in Affiliated Hospital of Hebei University were selected. Seventy patients with depression meeting the diagnostic criteria of ICD-10 were selected and randomly divided into control group and observation group using a random number table, with 35 patients in each group. The control group was treated with escitalopram, while the observation group was additionally treated with HBO on this basis. The patients were assessed using the Hamilton Depression Scale (HAMD) and Montreal Cognitive Assessment Scale (MoCA) before treatment and two, four and six weeks after treatment. Results: Two weeks after treatment, HAMD score showed a statistically significant difference between the two groups (P < 0.05). No statistically significant differences were found in HAMD score between the two groups four and six weeks after treatment (P > 0.05). Four and six weeks after treatment, MoCA score presented statistically significant differences between the two groups (P < 0.05). Conclusion: Escitalopram combined with HBO in the treatment of depression presents rapid efficacy and a certain effect in improving cognitive function. doi: https://doi.org/10.12669/pjms.37.4.3993 How to cite this:Mi K, Guo Q, Xu BY, Wang M, Bi H. Efficacy of hyperbaric oxygen combined with escitalopram in depression and its effect on cognitive function. Pak J Med Sci. 2021;37(4):1054-1057. doi: https://doi.org/10.12669/pjms.37.4.3993 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.001 | 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 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".