Real-world Experience to Understand the Use and Efficacy of Sebamed® Anti-Hair-Loss Shampoo in Managing Hair Fall
Bibliographic record
Abstract
Objective: The present questionnaire-based study aimed to investigate the clinical experience and treatment patterns of Sebamed® anti-hair-loss shampoo usage among patients with complaints of hair loss in real-world Indian settings. Materials and methods: An observational real-world, case- and questionnaire-based survey was conducted at 47 sites in Indian healthcare centres having medical records of patients with hair loss who had received Sebamed® anti-hair-loss shampoo therapy. Results: Emotional stress (38.4%), pollution (34.2%) and dietary insufficiency (33.7%) were common risk factors associated with hair fall, followed by sunlight exposure (26.7%), recent childbirth (12.2%), seasonal variations (11.5%) and high fever (6.6%). The patients with complaints of hairfall, majority of them had anti-hypertensive (32.3%) and hormonal therapy (28.5%) as concomitant medications. The majority of patients had excessive hair shedding. Hair-shedding score: (66.9%), while the remaining patients had normal hair shedding Hair-shedding score (33.1%) [1-9]. A total of 146 patients had other anti-hair loss therapy before the initiation of Sebamed® anti-hair loss shampoo. The majority of Healthcare Professionals (HCPs) expressed strong agreement that using Sebamed® anti-hair loss shampoo for 16 weeks led to improved hair growth, thickness, hair fall count and density in patients experiencing normal hair shedding and excessive hair shedding. The overall global assessment for tolerability was good to excellent for a majority of the patients (96.4%). Conclusion: The Sebamed® anti-hair fall shampoo presents a viable and secure option for managing hair thinning and promoting hair growth. Keywords: Emotional Stress; Hair Thinning; Shedd
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".